
With the rise in the popularity of Internet of Things (IoT) in-home health monitoring, the demand of data processing and analysis increases at the server. This is especially true for ECG data which has to be collected and analyzed continuously in real time. The data transmission and storage capacity of a simple home-use IoT system is often limited. In order to provide a responsive and reasonably high-resolution analysis over the data, the ECG recorder sampling rate must be tuned to an acceptable level such as 50Hz (compared to between 100Hz and 500Hz in lab), a huge amount of time series are to be gathered and dealt with. Therefore, a suitable sampling method that helps shorten the ECG data transformation time and uploading time is very important for cost saving.. In this paper, how to down sample the ECG data is investigated; instead of traditional data sampling methods, the use of a novel Brick-up Metaheuristic Optimization Algorithm (BMOA) that automatically optimizes the sampling of ECG data is proposed. By its adaptive design in choosing the most appropriate components, BMOA can build in real-time a best metaheuristic optimization algorithm for each device user assuming no two ECG data series are exactly identical. This dynamic pre-processing approach ensures each time the most optimal part of the ECG data series is harvested for health analysis from the raw data, in different scenarios from different users. In this study various application scenarios using real ECG datasets are simulated. The experimentation is tested with one of the most commonly used ECG classification methods, Long Short-Term Memory Network. The result shows the ECG data sampling by BMOA is indeed adaptive, the classification efficiency is improved, and the data storage requirement is reduced.
Citation: Qun Song, Tengyue Li, Simon Fong, Feng Wu. An ECG data sampling method for home-use IoT ECG monitor system optimization based on brick-up metaheuristic algorithm[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 9076-9093. doi: 10.3934/mbe.2021447
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With the rise in the popularity of Internet of Things (IoT) in-home health monitoring, the demand of data processing and analysis increases at the server. This is especially true for ECG data which has to be collected and analyzed continuously in real time. The data transmission and storage capacity of a simple home-use IoT system is often limited. In order to provide a responsive and reasonably high-resolution analysis over the data, the ECG recorder sampling rate must be tuned to an acceptable level such as 50Hz (compared to between 100Hz and 500Hz in lab), a huge amount of time series are to be gathered and dealt with. Therefore, a suitable sampling method that helps shorten the ECG data transformation time and uploading time is very important for cost saving.. In this paper, how to down sample the ECG data is investigated; instead of traditional data sampling methods, the use of a novel Brick-up Metaheuristic Optimization Algorithm (BMOA) that automatically optimizes the sampling of ECG data is proposed. By its adaptive design in choosing the most appropriate components, BMOA can build in real-time a best metaheuristic optimization algorithm for each device user assuming no two ECG data series are exactly identical. This dynamic pre-processing approach ensures each time the most optimal part of the ECG data series is harvested for health analysis from the raw data, in different scenarios from different users. In this study various application scenarios using real ECG datasets are simulated. The experimentation is tested with one of the most commonly used ECG classification methods, Long Short-Term Memory Network. The result shows the ECG data sampling by BMOA is indeed adaptive, the classification efficiency is improved, and the data storage requirement is reduced.
Chronic kidney disease (CKD) is a healthcare burden due to the high economic costs it generates for health systems and its high incidence and prevalence. The mortality of CKD has increased in the last 10 years. It is currently the 12th most common cause of death according to the Global Burden of Disease Study in 2015 and also one of the fastest rising major causes of mortality, along with diabetes and dementia [1]–[3]. CKD is defined as abnormalities of kidney structure or function present for over 3 months with specific implications for health [4]. An expanded definition of CKD includes a glomerular filtration rate of less than 60 mL/min/1.73m2 and a 1-time urine albumin-creatinine ratio of at least 30 mg/g with or without kidney damage, or more markers of kidney failure [4]–[6]. The clinical progression of the disease is described in 5 stages. In the most advanced stage, kidney replacement therapy is proposed to patients in the form of hemodialysis or peritoneal dialysis [7].
CKD is associated with many health consequences, including metabolic, endocrine and cardiovascular alterations. It is also strongly associated with pulmonary edema and respiratory muscle dysfunction, leading to a high risk of lung dysfunction in affected patients. The prevalence of lung dysfunction increases in CKD patients from stages 1 to 4 according to the National Health and Nutrition Examination Survey (NHANES) 2007–2012 [8],[9].
Due to its progressive nature, CKD affects patients in many spheres along the course of the disease including physical, mental and emotional well-being. It changes their daily living and social participation and decreases their perception of quality of life (QOL). Patients with CKD have to change their lifestyles, habits and nutrition and adjust to medical treatments and physical limitations. They experience existential and emotional conflicts, among other health situations and biopsychosocial changes that negatively impact their QOL [7]. In addition, QOL is a marker of disease burden and the assessment of QOL is an important criterion of the effectiveness of many treatments and interventions in health care and a predictor for adverse outcomes [10],[11].
According to the World Health Organization (WHO) [12] and the American College of Sports Medicine [13], regular exercise training has been proven to help populations maintain healthy levels of quality of life, mitigating health risks. It is also considered safe for adults living with the selected chronic conditions.
Different exercise modes are very popular in the health and fitness industry at European [14] and global level [15]. Respiratory training has been included in trendy exercise modes and is currently used in a wide range of populations [16],[17]. In fact, breathing training techniques have shown positive effects on health in patients with different conditions such as chronic obstructive pulmonary disease (COPD), asthma, postoperative pulmonary function, and cardiorespiratory function, among others [18].
However, it is unclear what kind of information is available in the literature about the effects of breathing training on patients with CKD in need of dialysis treatment. For these reasons, a scoping review was conducted to systematically map the research conducted in this area and identify any existing gaps in knowledge.
The objectives of this study were to examine the characteristics related to the application of breathing training on patients with CKD, and to identify the relevant outcomes and target group for the application of breathing training. Additionally, this scoping review was aimed at developing and confirming our prior inclusion criteria to ensure that the question asked by the subsequent systematic review could be answered by available and relevant evidence.
This scoping review is reported following the Preferred Reporting Items for Scoping Reviews (PRISMA ScR) guidelines [19] and was registered in the Prospective Register of Systematic Reviews (PROSPERO) with identification number CRD42021288231. Additionally, we followed the method suggested by Arksey and O'Malley [20] as standard steps for the development of scoping reviews.
We applied the recommended use of the PCC mnemonic (Population, Concept and Context) to guide question development [21]. The inclusion criteria were (1) patients with chronic kidney disease (2) who received breathing training programs and (3) the breathing training intervention had to be compared to a control group that received usual care or no treatment.
We conducted a broad search of the literature for indexed articles on electronic databases MEDLINE/PubMed, Web of Science and Scopus from their inception to March 2022. The search strategy was designed using the following steps: (1) examining relevant key terms used in existing systematic reviews to develop our keywords, (2) a thorough search for terms in the MeSH Database, (3) and expert guidance by a specialist. The strategy was adapted to index across other databases. We screened the references of relevant reviews to screen for additional studies that could potentially be included in this scoping review. The full search strategy is shown in Appendix A.
All the searched citations were stored in the Mendeley Desktop 1.19.4 reference manager application. Duplicated studies retrieved from electronic searches were removed. Two independent researchers screened the titles and abstracts of articles found in the searches (A.I.R., A.H.C.). Studies appearing to meet the inclusion criteria and those with insufficient data to make a clear decision were selected for evaluation of the full manuscript to determine their eligibility. Disagreement was solved by a third researcher (C.V.).
We charted key items of information obtained from the primary research reports reviewed. Data extraction was performed by one of the researchers through a custom-designed data extraction form created in Excel (Microsoft Corporation, Redmond, WA), using ‘data charting form’. This form included information on the study population, the type of intervention and the outcome measures employed. We recorded information as follows:
Two authors independently assessed the methodological quality and the risk of bias of individual studies. We used the Downs and Black Checklist [22] to assess methodological quality. This assessment method includes 27 items in five subscales (study quality, external validity, study bias, confounding and selection bias, and study power). It classifies the quality of studies as follows: excellent when scoring 26 or more points, good between 20 and 25 points, fair between 15 and 19, and poor when the score is 14 or less. Due to its high validity and reliability, this scale is one of the most suitable scales for use in research reviews [23],[24].
The risk of bias was assessed with the Cochrane risk-of-bias tool for randomized control trials [25]. The items of this tool classify the risk of bias as high when the methodological procedure is not described, unclear if the description is unclear, and low when the procedure is described in detail. A study is considered to have good quality when all criteria are met and fair quality when one criterion is not met or two criteria are unclear, and there is no known important limitation that could invalidate the results. It is considered to have poor quality when two or more criteria are listed as having high or unclear risk of bias, or when one criterion is not met or two criteria are unclear and there are important limitations that could invalidate the results [26].
Our search strategy identified 796 potentially eligible articles from MEDLINE/PubMed, Web of Science and Scopus databases. After removal of duplicates and studies with animals, 182 titles and abstracts were screened for potentially relevant articles. Fourteen studies were selected for full-text evaluation. Finally, 4 papers were included in the scoping review [27]–[30]. Details of the study selection procedure are listed in Figure 1.
The results of the methodological quality of studies included are shown in Table 1 The risk of bias in all these studies ranged from 23 to 26 points. Only the study by Tsai et al. [27] had a good quality score, and the studies by Huang et al. [29] and Kharbteng et al. [28] had a poor quality score.
A total of 206 subjects with CKD were assessed in the studies included, and 60.5% were male. The experimental groups included 92 patients aged between 52 and 66 years, and the control group included 86 patients aged between 51 and 61 years. Of the studies included, two were conducted in Taiwan [27],[29] and one was conducted in India [28]. The kidney disease stage of the patients was heterogeneous. One study included patients with CKD who received hemodialysis in two or three three-hour sessions weekly for more than three months [27], two studies [28],[30] included patients without kidney replacement therapy (KRT) who had a clinically stable course for the last month and an estimated glomerular filtration rate (GFR) between 14 and 45 ml/min/1.73m2 [27] and one study included patients with kidney failure undergoing hemodialysis treatment three times per week for at least three months [29].
Details about applied interventions and obtained results are reported in Table 2. Breathing training programs were applied heterogeneously, that is, isolated or combined; three studies [27],[28],[30] applied isolated breathing training, and one study [29] combined breathing training and leg exercises.
The components of the usual care in the control groups were also heterogeneous. Tsai et al. [27] assigned patients in the control group to a waiting list and after the post-test measurements were completed, the control group received breathing training for four weeks. Kharbteng et al. [28] did not specify the usual care components. Huang et al. [29] described usual care including routine medications, medical treatment, and guidance regarding diet, daily activity and water restrictions.
The duration and timing of training also varied among studies. Tsai et al. [27] and Kaneko et al. [30] designed a four-week intervention program with breathing exercises, twice weekly for a total of eight sessions with no specifications of the timing related to treatment. The intervention used by Kharbteng et al. [28] consisted of 5-minute sessions three times a day for 4 weeks. Participants in the study by Huang et al. [29] underwent a 12-week intervention three times per week performed two hours after hemodialysis was initiated.
Outcome measures
Quality of life was the main outcome measure, and it was measured with different instruments in the studies analyzed. One study [27] assessed health-related quality of life using the Medical Outcome Studies 36-Short Form Health Survey (SF-36). Another study [28] measured quality of life with the Kidney Disease and Quality of Life questionnaire (KDQOL™-36). Finally, a study [29] used the Chinese version of the World Health Organization quality of life assessment brief to reflect quality of life and general health status.
There were also other outcomes measured in the studies included in this scoping review such as depression measured with the Beck Depression Inventory-II (BDI-II) and self-reported sleep quality assessed using the Pittsburg Sleep Quality Index (PSQI) [27], as well as heart rate variability and fatigue assessed with the hemodialysis-related fatigue scale [29],[30]. The study by Kaneko et al. [30] also assessed blood pressure, respiratory rate, skin temperature, and skin blood flow.
The analyzed studies showed significant improvements in quality of life after treatment intervention. Tsai et al. [27] reported that the intervention group had scores significantly higher than the control group for both the role-emotional subscale and the mental component summary of the 36-Item Short-Form Health Survey (SF-36). In their study, Kharbteng et al. [28] found a significant difference in mean scores in the intervention group for the KDQOL™-36 for the subscales effects of kidney disease, SF-12 physical functioning or physical health component, and SF-12 mental functioning or mental health composite. Huang et al. [29] found significant changes in quality of life after the intervention in the experimental group.
Additionally, the study by Tsai et al. [27] showed a significant decrease in depressive symptoms after treatment but no changes in sleep quality. Huang et al. [29] found significant decreases in fatigue but no significant changes in heart rate variability. The study by Kaneko et al. [30] also showed significant differences in diastolic BP, respiratory rate, skin temperature, HF, and the LF/HF ratio, after applying the breathing intervention. The details of interventions and obtained results are reported in Table 2.
Study (year) | Design | Pathology treatment status | Sample (% male) | Sample Age Years ± SD | Quality of assessment Downs and Black (risk of bias) |
Huang et al. (2021) [29] | RCT | KF In hemodialysis 3 times / week At least 3 months |
EG: n = 40 (72.5%) CG: n = 43 (65.1%) Total: n = 83 (68.67%) |
EG: 53.70 ± 10.04 CG: 61.19 ± 10.19 |
24 (Poor quality) |
Kharbteng et al. (2020) [28] | RCT | CKD without KRT Clinically stable course for at least 1 month |
EG: n = 30 (50%) CG: n = 30 (70%) Total: n = 60 (60%) |
EG: 52.06 ± 6.97 CG: 51.83 ± 10.27 |
23 (Poor quality) |
Tsai et al. (2015) [27] | RCT | CKD In hemodialysis 2/3 times / week 3 hours / season At least 3 months |
EG: n = 32 (50%) CG: n = 25 (48%) Total: n = 57 (49.12%) |
EG: 64.94 ± 9.51 CG: 61.08 ± 11.18 |
26 (Good quality) |
Kaneko et al (2021) [30] | Pilot quasi-experimental study | CKD without KRT in a stable condition |
EG: n = 6 (100%) CG: - |
EG: 66.0 ± 9.4 CG: - |
- |
*Note: RCT – Randomized controlled trial; KF – Kidney failure; CKD – Chronic kidney disease; KRT – Kidney replacement therapy; EG – Experimental group; CG – Control group; SD – Standard deviation.
Study (year) | Timing of intervention | Interventions | Outcomes | Main results |
Huang et al. (2021) [29] | During hemodialysis sessions (3 hours) 12 weeks 3 times/week |
EG Usual care Breathing-based low-intensity leg exercise program leg lifts + quadriceps femoris contraction + knee flexion + five abdominal breaths 15 min/exercise section CG Usual care: routine medication, medical treatment and guidance (diet + daily activity + water restrictions) |
- QOL WHOQOL_BREF - Heart rate variability Low-frequency power is associated with the clinical response to sympathetic and parasympathetic activity and high-frequency power, which is an index of parasympathetic activity. - Fatigue The hemodialysis-related fatigue scale. |
↑ WHOQOL * ↓ Fatigue* LF X HF X |
Kharbteng et al. (2020) [28] | At home 4 weeks 7 times / week 3 times / day |
EG Breathing training program (alternate nostril breathing or anulom-vilom) 4-7-8 breathing exercises and breath counting 5 min/session (15 min/day) CG Usual care |
- QOL KDQOL-36 |
↑KDQOL™-36* |
Tsai et al. (2015) [27] | NR (at the dialysis center) 4 weeks 2 times / week |
EG Audio device-guided breathing training 1st session:
7 following sessions:
CG Waiting list After the posttest measurements were completed, patients received four weeks of breathing training |
- QOL SF-36 - Depression BDI-II - Sleep quality PSQI |
↓ BDI-II * PSQI X ↑Role-emotional subscale and mental component summary of QoL FS-36* |
Kaneko et al (2021) [30] | NR Around 4 weeks 2 times / day |
EG Six abdominal breaths per minute for 15 minutes Subjects repeatedly inhaled for 3 seconds through the nose and exhaled for 6 seconds through the mouth. CG No control group |
-Heart rate -Blood pressure -Respiratory rate -Skin temperature -Skin blood flow -Heart rate variability: LF, HF, ratio of LF and HF power |
HR X Systolic BP X ↓ Diastolic BP* ↓ Respiratory rate* ↑Skin temperature* Skin blood flow X LF X ↑ HF* ↓ LF/HF ratio* |
*Note: EG – Experimental group; CG – Control group; NR – Not reported; QOL – Quality of life; WHOQOL_BREF – World Health Organization quality of life-brief version; LF – Low-frequency power; HF – High-frequency power; KDQOL-36 – Kidney Disease and Quality of Life questionnaire; SF-36 – Medical Outcome Studies 36-Item Short Form Health Survey; BDI-II – Beck Depression Inventory II; PSQI – Pittsburgh Sleep Quality Index; BP – Blood pressure; *: Statistically significant; ↑: Increment; ↓: Decrement; X: No statistically relevant variations.
To our knowledge, this is the first scoping review to evaluate the effects of breathing training on patients with CKD treated by dialysis. The small number of included studies and the publication years indicate the novelty and limited research to date. Even with the heterogeneity of the studies included, our findings suggest that breathing training alone or combined with leg exercises has positive effects on quality of life in CKD patients without KRT or hemodialysis treatment.
Even the analysed studies used different approaches to breathing training program design and choice of technique; all of them included abdominal breathing, a breathing exercise that seemed to have positive effects on quality of life. The four included studies [27]–[30] followed similar coaching method for teaching their breathing training programs to participants in the experimental group. The experimental group in all the studies [27]–[30] received a coaching training demonstration by the researchers. In addition, to enhance the intervention performance, one study used pre-recorded instructions to guide each session [27]; in another two studies, the experimental group was guided with a video provided to each participant in the experimental group with the purpose of either standardizing the program and correcting the practice [29] or practicing the exercises at home [28]. This methodology could also have ensured adherence to treatment.
Even though the duration of the interventions and of the entire protocols were heterogeneous among the studies, the evidence in this scoping review suggests that a breathing training intervention as short as a total of 8 sessions in 4 weeks has positive benefits in some areas of the quality of life in CKD patients treated by hemodialysis [27],[30]. In this regard, other exercise types have demonstrated similar improvement in CKD quality of life and functional status, with greater values of TAC,CAT,GSH and GSH/GSSG after the exercise program [31].
Given that no special equipment was required, after coaching, respiratory training could be performed from home without taking much time, with good benefits as reported by Kharbteng et al. [28] This matches the findings of Lu et al. [32], which concluded that home-based breathing exercises have beneficial effects on chronic obstructive pulmonary disease.
The study by Tsai et al. [27] had self-reported depressive symptoms as primary outcome and the health-related quality of life and self-reported sleep quality as secondary outcomes. The latter outcome showed no statistically relevant variations, but the breathing program had positive and statistically relevant changes in the other two outcomes. Similarly, the study by Levendoglu et al. [33] showed a significant reduction of depression levels and the mental component scale of CKD patients after applying a twelve-week exercise program.
Other studies support our findings with breathing training as a promising intervention to improve health outcomes and quality of life in various pathologies such as heart failure [34] and chronic obstructive pulmonary disease [35].
The strength of our study is that it is the first to review the effects of breathing training on CKD patients. Additionally, it includes research published about the topic to date.
This scoping review has several limitations. Our analysis included a small number of studies; nevertheless, previous reviews have been conducted with a similar number of studies [36]. Additionally, the interventions of the studies included were not homogeneous, making it difficult to categorize the results.
In conclusion, a breathing training intervention for at least 4 weeks, including diaphragmatic breathing exercises, was able to improve the quality of life of patients with CKD during hemodialysis treatment.
These findings could improve the daily clinical practice of CKD healthcare professionals and the daily physical activity of CKD patients. It is a coaching training protocol that does not require extra equipment and could be used in the future as a non-invasive low-cost intervention for patients with CKD for improving their performance status and quality of life.
This scoping review was undertaken as a precursor to future systematic reviews that confirm the results shown here. In this regard, we performed a preliminary mapping of published literature that could be taken as a base for clinical practice. In addition, it is necessary to conduct future randomized controlled trials using different breathing training programs in the various CKD stages.
[1] | L.-Y. Ma, W.-W. Chen, R.-L. Gao, L.-S. Liu, M.-L. Zhu, Y.-J. Wang, et al., China cardiovascular diseases report 2018: an updated summary, J. Geriatr. Cardiol., 17 (2020), 1–8. |
[2] |
M. Sanz, A. M. del Castillo, S. Jepsen, J. Gonzalez-Juanatey, F. D'Aiuto, P. Bouchard, et al., Periodontitis and cardiovascular diseases: Consensus report, J. Clin. Periodontol., 47 (2020), 268–288. doi: 10.1111/jcpe.13189
![]() |
[3] | S. Baumann, Evaluation of data usability generated by wearables & iot-enabled home use medical devices via telehealth to identify if blockchain can solve potential challenges, 2020. |
[4] | M. H. Nornaim, N. A. Abdul-Kadir, F. K. Harun, M. A. A. Razak, A wireless ecg device with mobile applications for android, In 7th Int. Conf. Electr. Eng. Comput. Sci. Inf., pages 168–171. IEEE, 2020. |
[5] |
A. K. Sangaiah, M. Arumugam, G.-B. Bian, An intelligent learning approach for improving ecg signal classification and arrhythmia analysis, Artif. Intell. Med., 103 (2020), 101788. doi: 10.1016/j.artmed.2019.101788
![]() |
[6] | P. M. Rautaharju, S. H. Zhou, E. W. Hancock, B. M. Hor, D. Q. Feild, J. M. Lindauer, et al., Comparability of 12-lead ecgs derived from easi leads with standard 12-lead ecgs in the classification of acute myocardial ischemia and old myocardial infarction, J. Electrocardiol., 35 (2002), 35–39. |
[7] |
Y. Zou, J. Han, X. Weng, X. Zeng, An ultra-low power qrs complex detection algorithm based on down-sampling wavelet transform, IEEE Signal Process. Lett., 20 (2013), 515–518. doi: 10.1109/LSP.2013.2254475
![]() |
[8] |
L. Mesin. Heartbeat monitoring from adaptively down-sampled electrocardiogram, Comput. Biol. Med., 84 (2017), 217–225. doi: 10.1016/j.compbiomed.2017.03.023
![]() |
[9] | Q. Song, S. Fong, Brick-up metaheuristic algorithms, In 5th IIAI Int. Congress Adv. Appl. Inf., pages 583–587. IEEE, 2016. |
[10] | L. S. Lilly, Pathophysiology of heart disease: a collaborative project of medical students and faculty, Lippincott Williams & Wilkins, 2012. |
[11] |
C. Zhang, Y. Chen, A. Yin, X. Wang, Anomaly detection in ecg based on trend symbolic aggregate approximation, Math. Biosci. Eng., 16 (2019), 2154–2167. doi: 10.3934/mbe.2019105
![]() |
[12] |
S. Mitra, M. Mitra, B. B. Chaudhuri, Generation of digital time database from paper ecg records and fourier transform-based analysis for disease identification, Comput. Biol. Med., 34 (2004), 551–560. doi: 10.1016/j.compbiomed.2003.08.001
![]() |
[13] |
R. J. Martis, U. R. Acharya, L. C. Min, Ecg beat classification using pca, lda, ica and discrete wavelet transform, Biomed. Signal Process. Control, 8 (2013), 437–448. doi: 10.1016/j.bspc.2013.01.005
![]() |
[14] |
J.-J. Wei, C.-J. Chang, N.-K. Chou, G.-J. Jan, Ecg data compression using truncated singular value decomposition, IEEE Trans. Inf. Technol. Biomed., 5 (2001), 290–299. doi: 10.1109/4233.966104
![]() |
[15] |
S. Fong, X. Wang, Q. Xu, R. Wong, J. Fiaidhi, S. Mohammed, Recent advances in metaheuristic algorithms: Does the makara dragon exist?, J. Supercomput., 72 (2016), 3764–3786. doi: 10.1007/s11227-015-1592-8
![]() |
[16] | W. Li, G.-G. Wang, A. H. Gandomi, A survey of learning-based intelligent optimization algorithms, Arch. Comput. Method. E., (2021), pages 1–19, 2021. |
[17] | S. Mirjalili, Genetic algorithm, In Evolutionary algorithms and neural networks, pages 43–55. Springer, 2019. |
[18] |
X.-S. Yang, X. He, Bat algorithm: literature review and applications, Int. J. Bio-inspir. Com., 5 (2013), 141–149. doi: 10.1504/IJBIC.2013.055093
![]() |
[19] | D. J. Li, C. Z. Qiang, Y. Z. Zhi, On the combination of genetic algorithm and ant algorithm, J. Comput. Inf. Syst., 9 (2003), 10. |
[20] | R. Tang, S. Fong, X.-S. Yang, S. Deb, Wolf search algorithm with ephemeral memory, In 7th Int. Conf. Digit. Inf. Management, pages 165–172. IEEE, 2012. |
[21] | K. Premalatha, A. Natarajan, Hybrid pso and ga for global maximization, Int. J. Open Problems Compt. Math, 2 (2009), 597–608. |
[22] | B. Mendzelevski, C. S. Spencer, A. Freier, D. Camilleri, C. Graff, J. Täubel, Comparing the consistency of electrocardiogram interval measurements by resting ecg versus 12-lead holter, Ann. Noninvas. Electro., page e12851, 2021. |
[23] | R. T. Olszewski, Generalized feature extraction for structural pattern recognition in time-series data, Carnegie Mellon University, 2001. |
[24] |
E. K. Wang, L. Xi, R. P. Sun, F. Wang, L. Y. Pan, C. X. Cheng, et al., A new deep learning model for assisted diagnosis on electrocardiogram, Math. Biosci. Eng., 16 (2019), 2481–2491. doi: 10.3934/mbe.2019124
![]() |
[25] |
S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Comput., 9 (1997), 1735–1780. doi: 10.1162/neco.1997.9.8.1735
![]() |
[26] | P. Malhotra, L. Vig, G. Shroff, P. Agarwal, Long short term memory networks for anomaly detection in time series, In Proceedings, volume 89, pages 89–94. Presses universitaires de Louvain, 2015. |
[27] | K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, J. Schmidhuber, Lstm: A search space odyssey, IEEE Trans. Neural Netw. Learn. Syst., 28 (2016), 2222–2232. |
[28] | S. Chauhan, L. Vig, Anomaly detection in ecg time signals via deep long short-term memory networks, In IEEE Int. Conf. Data Sci. Adv. Anal., pages 1–7. IEEE, 2015. |
[29] | M. Liu, Y. Kim, Classification of heart diseases based on ecg signals using long short-term memory, In 40th Ann. Int. Conf. IEEE Eng. Med. Biol. Soc., pages 2707–2710. IEEE, 2018. |
[30] |
B. H. D. Koh, C. L. P. Lim, H. Rahimi, W. L. Woo, B. Gao. Deep temporal convolution network for time series classification, Sensors, 21 (2021), 603. doi: 10.3390/s21020603
![]() |
[31] | C. L. P. Lim, W. L. Woo, S. S. Dlay, Enhanced wavelet transformation for feature extraction in highly variated ecg signal, In 2nd IET Int. Conf. Intell. Signal Process, pages 1–6. IET, 2015. |
[32] | C. L. P. Lim, W. L. Woo, S. S. Dlay, B. Gao, Heartrate-dependent heartwave biometric identification with thresholding-based gmm–hmm methodology, IEEE Trans. Ind. Inf., 15 (2018), 45–53. |
[33] | C. L. P. Lim, W. L. Woo, S. S. Dlay, D. Wu, B. Gao, Deep multiview heartwave authentication, IEEE Trans. Ind. Inf., 15 (2018), 777–786. |
[34] |
H. Guedri, A. Bajahzar, H. Belmabrouk, Ecg compression with douglas-peucker algorithm and fractal interpolation, Math. Biosci. Eng., 18 (2021), 3502–3520. doi: 10.3934/mbe.2021176
![]() |
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Study (year) | Design | Pathology treatment status | Sample (% male) | Sample Age Years ± SD | Quality of assessment Downs and Black (risk of bias) |
Huang et al. (2021) [29] | RCT | KF In hemodialysis 3 times / week At least 3 months |
EG: n = 40 (72.5%) CG: n = 43 (65.1%) Total: n = 83 (68.67%) |
EG: 53.70 ± 10.04 CG: 61.19 ± 10.19 |
24 (Poor quality) |
Kharbteng et al. (2020) [28] | RCT | CKD without KRT Clinically stable course for at least 1 month |
EG: n = 30 (50%) CG: n = 30 (70%) Total: n = 60 (60%) |
EG: 52.06 ± 6.97 CG: 51.83 ± 10.27 |
23 (Poor quality) |
Tsai et al. (2015) [27] | RCT | CKD In hemodialysis 2/3 times / week 3 hours / season At least 3 months |
EG: n = 32 (50%) CG: n = 25 (48%) Total: n = 57 (49.12%) |
EG: 64.94 ± 9.51 CG: 61.08 ± 11.18 |
26 (Good quality) |
Kaneko et al (2021) [30] | Pilot quasi-experimental study | CKD without KRT in a stable condition |
EG: n = 6 (100%) CG: - |
EG: 66.0 ± 9.4 CG: - |
- |
*Note: RCT – Randomized controlled trial; KF – Kidney failure; CKD – Chronic kidney disease; KRT – Kidney replacement therapy; EG – Experimental group; CG – Control group; SD – Standard deviation.
Study (year) | Timing of intervention | Interventions | Outcomes | Main results |
Huang et al. (2021) [29] | During hemodialysis sessions (3 hours) 12 weeks 3 times/week |
EG Usual care Breathing-based low-intensity leg exercise program leg lifts + quadriceps femoris contraction + knee flexion + five abdominal breaths 15 min/exercise section CG Usual care: routine medication, medical treatment and guidance (diet + daily activity + water restrictions) |
- QOL WHOQOL_BREF - Heart rate variability Low-frequency power is associated with the clinical response to sympathetic and parasympathetic activity and high-frequency power, which is an index of parasympathetic activity. - Fatigue The hemodialysis-related fatigue scale. |
↑ WHOQOL * ↓ Fatigue* LF X HF X |
Kharbteng et al. (2020) [28] | At home 4 weeks 7 times / week 3 times / day |
EG Breathing training program (alternate nostril breathing or anulom-vilom) 4-7-8 breathing exercises and breath counting 5 min/session (15 min/day) CG Usual care |
- QOL KDQOL-36 |
↑KDQOL™-36* |
Tsai et al. (2015) [27] | NR (at the dialysis center) 4 weeks 2 times / week |
EG Audio device-guided breathing training 1st session:
7 following sessions:
CG Waiting list After the posttest measurements were completed, patients received four weeks of breathing training |
- QOL SF-36 - Depression BDI-II - Sleep quality PSQI |
↓ BDI-II * PSQI X ↑Role-emotional subscale and mental component summary of QoL FS-36* |
Kaneko et al (2021) [30] | NR Around 4 weeks 2 times / day |
EG Six abdominal breaths per minute for 15 minutes Subjects repeatedly inhaled for 3 seconds through the nose and exhaled for 6 seconds through the mouth. CG No control group |
-Heart rate -Blood pressure -Respiratory rate -Skin temperature -Skin blood flow -Heart rate variability: LF, HF, ratio of LF and HF power |
HR X Systolic BP X ↓ Diastolic BP* ↓ Respiratory rate* ↑Skin temperature* Skin blood flow X LF X ↑ HF* ↓ LF/HF ratio* |
*Note: EG – Experimental group; CG – Control group; NR – Not reported; QOL – Quality of life; WHOQOL_BREF – World Health Organization quality of life-brief version; LF – Low-frequency power; HF – High-frequency power; KDQOL-36 – Kidney Disease and Quality of Life questionnaire; SF-36 – Medical Outcome Studies 36-Item Short Form Health Survey; BDI-II – Beck Depression Inventory II; PSQI – Pittsburgh Sleep Quality Index; BP – Blood pressure; *: Statistically significant; ↑: Increment; ↓: Decrement; X: No statistically relevant variations.
Study (year) | Design | Pathology treatment status | Sample (% male) | Sample Age Years ± SD | Quality of assessment Downs and Black (risk of bias) |
Huang et al. (2021) [29] | RCT | KF In hemodialysis 3 times / week At least 3 months |
EG: n = 40 (72.5%) CG: n = 43 (65.1%) Total: n = 83 (68.67%) |
EG: 53.70 ± 10.04 CG: 61.19 ± 10.19 |
24 (Poor quality) |
Kharbteng et al. (2020) [28] | RCT | CKD without KRT Clinically stable course for at least 1 month |
EG: n = 30 (50%) CG: n = 30 (70%) Total: n = 60 (60%) |
EG: 52.06 ± 6.97 CG: 51.83 ± 10.27 |
23 (Poor quality) |
Tsai et al. (2015) [27] | RCT | CKD In hemodialysis 2/3 times / week 3 hours / season At least 3 months |
EG: n = 32 (50%) CG: n = 25 (48%) Total: n = 57 (49.12%) |
EG: 64.94 ± 9.51 CG: 61.08 ± 11.18 |
26 (Good quality) |
Kaneko et al (2021) [30] | Pilot quasi-experimental study | CKD without KRT in a stable condition |
EG: n = 6 (100%) CG: - |
EG: 66.0 ± 9.4 CG: - |
- |
Study (year) | Timing of intervention | Interventions | Outcomes | Main results |
Huang et al. (2021) [29] | During hemodialysis sessions (3 hours) 12 weeks 3 times/week |
EG Usual care Breathing-based low-intensity leg exercise program leg lifts + quadriceps femoris contraction + knee flexion + five abdominal breaths 15 min/exercise section CG Usual care: routine medication, medical treatment and guidance (diet + daily activity + water restrictions) |
- QOL WHOQOL_BREF - Heart rate variability Low-frequency power is associated with the clinical response to sympathetic and parasympathetic activity and high-frequency power, which is an index of parasympathetic activity. - Fatigue The hemodialysis-related fatigue scale. |
↑ WHOQOL * ↓ Fatigue* LF X HF X |
Kharbteng et al. (2020) [28] | At home 4 weeks 7 times / week 3 times / day |
EG Breathing training program (alternate nostril breathing or anulom-vilom) 4-7-8 breathing exercises and breath counting 5 min/session (15 min/day) CG Usual care |
- QOL KDQOL-36 |
↑KDQOL™-36* |
Tsai et al. (2015) [27] | NR (at the dialysis center) 4 weeks 2 times / week |
EG Audio device-guided breathing training 1st session:
7 following sessions:
CG Waiting list After the posttest measurements were completed, patients received four weeks of breathing training |
- QOL SF-36 - Depression BDI-II - Sleep quality PSQI |
↓ BDI-II * PSQI X ↑Role-emotional subscale and mental component summary of QoL FS-36* |
Kaneko et al (2021) [30] | NR Around 4 weeks 2 times / day |
EG Six abdominal breaths per minute for 15 minutes Subjects repeatedly inhaled for 3 seconds through the nose and exhaled for 6 seconds through the mouth. CG No control group |
-Heart rate -Blood pressure -Respiratory rate -Skin temperature -Skin blood flow -Heart rate variability: LF, HF, ratio of LF and HF power |
HR X Systolic BP X ↓ Diastolic BP* ↓ Respiratory rate* ↑Skin temperature* Skin blood flow X LF X ↑ HF* ↓ LF/HF ratio* |