Research article Special Issues

Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice

  • Received: 25 January 2021 Accepted: 07 March 2021 Published: 10 March 2021
  • Citation: Kranthi Kumar Lella, Alphonse Pja. Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice[J]. AIMS Public Health, 2021, 8(2): 240-264. doi: 10.3934/publichealth.2021019

    Related Papers:

    [1] Maria Pia Riccio, Gennaro Catone, Rosamaria Siracusano, Luisa Occhiati, Pia Bernardo, Emilia Sarnataro, Giuseppina Corrado, Carmela Bravaccio . Vitamin D deficiency is not related to eating habits in children with Autistic Spectrum Disorder. AIMS Public Health, 2020, 7(4): 792-803. doi: 10.3934/publichealth.2020061
    [2] Anastasia Stathopoulou, Georgios F. Fragkiadakis . Assessment of psychological distress and quality of life of family caregivers caring for patients with chronic diseases at home. AIMS Public Health, 2023, 10(2): 456-468. doi: 10.3934/publichealth.2023032
    [3] Dorota Zarnowiecki, Meaghan S Christian, James Dollman, Natalie Parletta, Charlotte E.L Evans, Janet E Cade . Comparison of school day eating behaviours of 8–11 year old children from Adelaide, South Australia, and London, England
    . AIMS Public Health, 2018, 5(4): 394-410. doi: 10.3934/publichealth.2018.4.394
    [4] Casey T. Harris, Kevin Fitzpatrick, Michael Niño, Priya Thelapurath, Grant Drawve . Examining disparities in the early adoption of Covid-19 personal mitigation across family structures. AIMS Public Health, 2022, 9(3): 589-605. doi: 10.3934/publichealth.2022041
    [5] Christos Sikaras, Maria Tsironi, Sofia Zyga, Aspasia Panagiotou . Anxiety, insomnia and family support in nurses, two years after the onset of the pandemic crisis. AIMS Public Health, 2023, 10(2): 252-267. doi: 10.3934/publichealth.2023019
    [6] Waled Amen Mohammed Ahmed, Sara Boutros Shokai, Insaf Hassan Abduelkhair, Amira Yahia Boshra . Factors Affecting Utilization of Family Planning Services in a Post-Conflict Setting, South Sudan: A Qualitative Study. AIMS Public Health, 2015, 2(4): 655-666. doi: 10.3934/publichealth.2015.4.655
    [7] Erin Nolen, Catherine Cubbin, Mackenzie Brewer . The effect of maternal food insecurity transitions on housing insecurity in a population-based sample of mothers of young children. AIMS Public Health, 2022, 9(1): 1-16. doi: 10.3934/publichealth.2022001
    [8] J. Nwando Olayiwola, Melanie Raffoul . Saving Women, Saving Families: An Ecological Approach to Optimizing the Health of Women Refugees with S.M.A.R.T Primary Care. AIMS Public Health, 2016, 3(2): 357-374. doi: 10.3934/publichealth.2016.2.357
    [9] Dominique Meekers , Raseliarison Ratovonanahary , Tokinirina Andrianantoandro , Hiangotiana Randrianarisoa . Using Survey Data to Identify Opportunities to Reach Women with An Unmet Need for Family Planning: The Example of Madagascar. AIMS Public Health, 2016, 3(3): 629-643. doi: 10.3934/publichealth.2016.3.629
    [10] Argyro Pachi, Maria Anagnostopoulou, Athanasios Antoniou, Styliani Maria Papageorgiou, Effrosyni Tsomaka, Christos Sikaras, Ioannis Ilias, Athanasios Tselebis . Family support, anger and aggression in health workers during the first wave of the pandemic. AIMS Public Health, 2023, 10(3): 524-537. doi: 10.3934/publichealth.2023037

  • Abbreviations

    CNN:

    Convolutional Neural Network; 

    DDAE:

    Data De-Noising Auto Encoder; 

    MFCC:

    Mel-frequency Cepstral Coefficient; 

    DL:

    Deep Learning; 

    ML:

    Machine Learning; 

    AI:

    Artificial Intelligence; 

    SVM:

    Support Vector Machine; 

    LVQ:

    Learning Vector Quantization; 

    MLR:

    Multivariate Linear Regression; 

    MRI:

    Magnetic Resonance Imaging; 

    SSP:

    Speech Signal Processing; 

    LSTM:

    Long Short-Term Memory; 

    TDSN:

    Tensor Deep Stacking Network; 

    CRD:

    Compression of Range Dynamically; 

    BN:

    Background Noise; 

    ST:

    Stretching Time; 

    SP:

    Shift Pitch; 

    ReLU:

    Rectified Linear Unit; 

    MUDA:

    Musical Data Augmentation; 

    JAMS:

    JSON Annotated Music Specification

    Autism Spectrum Disorder (ASD) is a disease described as strongly heterogeneous due to the large number of symptoms which may appear in the child's functioning [1], as well as the variable response of the body to the treatment process [1],[2]. In spite of the fact that the symptoms are multiple and occur with changing intensity, every person with autism presents abnormalities in communication and social interaction [2], exhibits repetitive behaviours, and a limited scope of interests [3],[4]. The onset of the disease occurs in early childhood [3]. Only a minor percentage of people with diagnosed Autism Spectrum Disorder, with mild symptoms (ex. Difficulty in social communication, problematic with adaptation to change, planning difficulty) , are able to live a relatively independent life as adults [2],[3],[5]. The majority (with symptoms of moderate and severe intensity) need the help of their families or social welfare to the end of their lives [2]. Their functioning in adult life depends on the early introduction of intensive therapeutic programmes, modifying the undesirable behaviours, and aimed at teaching social and communication skills [6][8].

    Scientific literature stresses a constant growth in the incidence of the disorder under discussion. For example, data from the Autism and Developmental Disabilities Monitoring Network shows that, in 2012, in the USA, there were twice as many eight-year-old children with diagnosed ASD as only two years earlier, in 2010 [9]. Taking into consideration the whole population of children, in 2000, ASD was reported as occurring in one in every 150 children, and in 2010, in one of every 68 [10]. The causes of this situation are unknown. The scientists believe it is related to greater public awareness of the symptoms of autism, new diagnostic criteria, and possibility of diagnosis at a younger age [11][14]. These are only hypotheses, but they undoubtedly encourage various agencies-medical, social, educational and other to search for effective solutions for supporting people with autism and their families [4].

    The symptoms of autism are recognised in the child's environment quite quickly. Usually, it is the parents who first realise that their child does not achieve the expected milestones in development; his or her development is retarded or stopped [15][17]. At that time, parents observe that their child does not react to their physical affection, does not want to express emotions, often avoids hugging (which is very hard for parents, especially for mothers) and eye contact, and does not want to communicate in any way [15],[16]. Moreover, the child may present atypical behaviours, movements related to a strong need of isolation from its surroundings, which are incomprehensible to the parents [16]. Usually, these include destructive, socially unacceptable behaviours [18]. These symptoms arouse anxiety and feelings of helplessness in the parents and make them seek professional help [15].

    The problems affecting the autistic child affect also the parents. Therefore, it may be said that the autism of a child has considerable implications for its parents [19]. Caring for a child with autism is associated with emotional consequences [20][23]. It has been proved that parents of atypical children experience parental stress much more frequently than the general population [24], as the moment of the child's diagnosis generates strong uncertainty about the future life of the child and the whole family [25],[26].

    The study by Bitsik and Sharpley, conducted on the basis of an analysis of fathers and mothers of ASD children, showed that women are more preoccupied and prone to depression than men caring for their disabled children [27]. Similar results were obtained by Dąbrowska, who indicated that mothers are much more frequently exposed to stress [28][30]. Moreover, it was proved that parents of ASD children are three to five times more vulnerable to depression than parents of neurotypical children [31]. The most commonly used assessment tools for preoccupation and depression [27] of parents include:

    • Self-Rating Depression Scale—SDS [32].
    • Self-Rating Anxiety Scale—SAS [33].
    • Connor-Davidson Resilience Scale—CD-RISC [34].

    The aim of the paper is to evaluate the functioning of families with an ASD child and compare it to the functioning of families with neurotypical children. The degree of flexibility, cohesion and level of communication enables the family to be classified either as healthy or dysfunctional.

    The study was approved by Bioethics Committee of the Poznan Univeristy of Medical Sciences (approval number: 1223/17) and Australian New Zealand Clinical Trials Registry (ANZCTR) number ACTRN12618000598280.

    The study was performed using (Flexibility and Cohesion Evaluation Scales, FACES-IV) questionnaire by David H. Olson, in its Polish form, developed by Andrzej Margasiński. The questionnaire consists of sixty-two statements, to which the subject responds in a 5-degree scale, from strongly disagree to strongly agree. The statements are divided into eight sub-scales. Six of them are the main sub-scales of David H. Olson's Circumplex Model of the two dimensions of family functioning: cohesion and flexibility (Balanced Cohesion, Disengaged, Unmeshed, Balanced Flexibility, Rigid, Chaotic). The two remaining sub-scales measure family communication (which is the third dimension of the Circumplex Model) and family satisfaction. Apart from sub-scale results, it is possible to calculate three complex ratios: Cohesion Ratio, Flexibility Ratio and Total Circumplex Ratio, which reflects the degree to which family functioning is healthy [35].

    The tool used is based on the Circumplex Model, which focuses on three crucial dimensions of family functioning: cohesion, flexibility and communication. Cohesion means the emotional bonding that family members have towards one another. Flexibility of relationships is defined as the quality and expression of leadership and organization, role relationships, and relationships rules and negotiations [36]. The communication dimension is viewed as a facilitating dimension that helps families alter their levels of cohesion and flexibility. The intensity of cohesion and flexibility of family relationships may have two basic levels: balanced or unbalanced. Unbalanced cohesion may mean extremely high cohesion level (unmeshed relationships) or an extremely low cohesion level (disengaged relationships, lack of bonding). On the other hand, unbalanced flexibility may mean extremely high (chaotic family relationships) or extremely low (rigid family relationships) flexibility levels. The main hypothesis of the model says that there is a positive relationship between a balanced cohesion level, balanced flexibility level, and healthy family functioning, as well as a positive relationships between unbalanced cohesion level, unbalanced flexibility level and problematic family functioning [36].

    The third basic dimension of D. H. Olson's Circumplex Model, which influences both flexibility and cohesion, is communication [37]. This refers to the skill of providing the family members with information, plans and emotions. This dimension is also defined as the positive communication skills utilized in the couple or family system[38].

    Cluster analysis of data obtained from studies, using Flexibility and Cohesion Evaluation Scales, resulted in distinguishing six family types: Balanced, Cohesively Rigid, Flexibly Disengaged, Mid-range, Rigidly Disengaged and Unbalanced [35]. The Balanced type is characterised by the highest scores on the balanced sub-scales and the lowest scores on the remaining sub-scales. The Cohesively Rigid type is characterised by high scores in the balanced cohesion and rigid sub-scales, moderate enmeshed scores, and low disengaged and chaos scores. The Flexibly Disengaged type is characterised by high scores on the Balanced Flexibility and Disengaged sub-scales, and low scores on the Rigid sub-scale The Mid-range type is characterised by moderate scores on all of the sub-scales, with the exception of the disengaged sub-scale, where the score is usually low. The Rigidly Disengaged type is characterised by high scores on all of the sub-scales other than Cohesion, where moderate to low scores are characteristic. The Unbalanced type is characterised by high scores on all four of the unbalanced scales: Disengaged, Unmeshed, Rigid and Chaotic, and low scores on the two balanced scales: Balanced Cohesion and Balanced Flexibility. These families are assumed to experience the greatest difficulties and be the most problematic in terms of their functioning. It is estimated that this is the family type most often looking for therapy [35].

    The study with Flexibility and Cohesion Evaluation Scales, by David H. Olson, in its Polish adaptation by Andrzej Margasiński, included 70 parents of ASD children, and 70 parents with children without diagnosed ASD, as the control group. The study was performed in January and February 2018. The study used inclusion criteria: (1). parents aged 25–45; (2). children without comorbidities; (3). diagnosis of autism in children.

    In order to compare FACES IV results obtained by the parents of ASD children and the control group, an independent samples t-test for equality of means was performed, and the statistical significance of the obtained differences was assessed.

    The analysis of the Balanced Cohesion sub-scale indicated that the parents of children with autism achieve lower FACES-IV results in the Balanced Cohesion sub-scale than the control group. The study covered 140 observations. The significance level of Levene's test indicates that the results should be interpreted with the assumed equality of variance. The p-value for the t-test for difference of means is 0.002; therefore, the means in both groups differ in a statistically significant way. The results are presented in Table 1.

    Table 1.  The sub-scales in the group of parents of children with ASD vs. parents of neurotypical children.
    Group N Average P-value
    Balanced Cohesion sub-scale (STEN) Autism 70 5.2000 21,843
    Control group 70 6.3571 22,135
    Balanced Flexibility sub-scale (STEN) Autism 70 5.6857 20,820
    Control group 70 6.2143 19,478
    Disengaged sub-scale (STEN) Autism 70 7.2857 18,583
    Control group 70 6.4143 18,217
    Unmeshed sub-scale (STEN) Autism 70 6.8857 20,610
    Control group 70 5.4857 18,077
    Rigid sub-scale (STEN) Autism 70 6.9143 17,672
    Control group 70 6.6857 17,573
    Chaotic sub-scale (STEN) Autism 70 6.7143 18,893
    Control group 70 6.0143 19,597
    Family Communication sub-scale (STEN) Autism 70 5.3857 24,215
    Control group 70 6.1714 24,846
    Family Satisfaction sub-scale (STEN) Autism 70 6.3143 24,586
    Control group 70 7.2857 21,274

     | Show Table
    DownLoad: CSV

    • Balanced flexibility sub-scale The p-value for the t-test for difference of means is 0.123; therefore, the means in both groups do not differ in a statistically significant way.
    • Disengaged sub-scale The p-value for the t-test for difference of means is 0.006; therefore, the means in both groups differ in a statistically significant way.
    • Unmeshed sub-scale The p-value for the t-test for difference of means is 0.000; therefore, the means in both groups differ in a statistically significant way.
    • Rigid sub-scale The p-value for the t-test for difference of means is 0.444; therefore, the means in both groups do not differ in a statistically significant way.
    • Chaotic sub-scale The p-value for the t-test for difference of means is 0.033; therefore, the means in both groups differ in a statistically significant way.
    • Family communication sub-scale The p-value for the t-test for difference of means is 0.060; therefore, the means in both groups do not differ in a statistically significant way.
    • Family satisfaction sub-scale The p-value for the t-test for difference of means is 0.014; therefore, the means in both groups differ in a statistically significant way.

    The analyses within the group of parents of ASD children did not show any statistically significant differences in FACES-IV due to socio-demographic variables.

    Research into parental stress levels showed that parents of children with ASD have greater uncertainty, stress and depression levels than parents of neurotypical children [39][43] and also parents of children with other disabilities [44],[45]. Similar results can be observed in the comparison between the stress levels of parents of ASD children and the general population [21],[43],[46][49].

    The most significant factor generating parenting stress are the ASD symptoms in their children [31]. Among the most frequent symptoms contributing to parental stress, scientists enumerate impaired cognitive functions and impaired social reactions, which directly correspond to the emergence of parental stress, anxiety and depression [50][53]. Other aspects of autism which may induce parental stress are: the level of functioning of the child, the child's age, the dysfunction of adaptive behaviours, agammaession, tantrums, and self-inflicted injuries [21],[54][57].

    However, it is emphasized that there is no social understanding of the characteristics of ASD, due to which, both the parents and the ASD children themselves, are subject to more severe social criticism. The specific behaviours of ASD patients are often perceived as parenting errors [31]. What is even more important, it is considered that parental stress factors come exclusively from outside of this social group and not from the personality and behavioural models of the parents themselves [31],[58]. Important factors influencing the development of parental stress and burn-out include lack of activity of mothers of ASD children outside the home in comparison to mothers of neurotypical children, who can spend much more of their free time outside the family, in a stress-free environment [59]. Similar conclusions were made in other studies, which, apart from isolation factors, also identified the phenomenon of “self-blaming” mothers, who burden themselves with blame for their child's difficulties [60],[61]. Another aspect of parental stress, described in the literature, is escaping from the problems related to the child's disability, visible as its difficult behaviours [62].

    The assessment of parental stress showed that over a half (55.8%) of fathers feel overwhelming helplessness one to five times a month. On the other hand, over 70% of mothers experience this feeling one to five times a month. The results of this study confirmed earlier research into anxiety and depression in parents, conducted by the same authors on a group of parents in Australia [27],[63].

    Another study focused on the parents of children with diagnosed ASD. The research into parental stress showed that the majority of the subjects agreed to the statements that “caring for a child takes a lot of time and energy” and “the behaviour of my child embarrasses and stresses me”. In the area of social support, the majority of the subjects agreed with the statements “the members of my family rely on me” and “I cannot rely on the members of my family”. As far as the area of self-efficacy is concerned, the majority marked the answer “try another solution if the first one did not bring expected results”. The study described showed that there are multiple sources of parental stress and that its level is influenced by all members of the ADS child's family, including parents, siblings, and grandparents. It was also shown that, despite the difficulties and problems, the caregivers of ASD children have social support and can cope with difficult situations [64].

    The scientific literature also includes works devoted to the role of stress resilience and self-efficacy in parents of ASD children. One of them analyses the group under discussion. The study was conducted using the Satisfaction With Life Scale (SWLS) [65], the Coping Strategy Inventory (CSI) [66], and the Coping with Stress Self-Efficacy Scale (CSSES) [67]. The results confirm that bringing up a child influences coping strategies and the sense of self-efficacy. Therefore, stress has an impact on the level of satisfaction with life of parents of ASD children. The scientists found differences depending on the parent's sex, stating that the primary goal of a woman is the sense of self-efficacy, while men put problem solving in the first place [67][72]. It was also shown that, together with the ageing of ASD parents, the social support for these families decreases, as does cognitive restructuring [69],[73],[74].

    The results of many studies prove that the sense of self-efficacy contributes to higher life satisfaction. Moreover, the sense of self-efficacy correlates positively with resilience strategies (problem solving and cognitive restructuring) and negatively with dysfunctional strategies (social isolation, wishful thinking, self-criticism) [60],[69],[75][79].

    It is worth mentioning the study conducted by Bitsik et al. (2017), analysing daily cortisol levels in parents of ASD children. Cortisol is called the neurohormone of stress [57],[80]. Cortisol levels were measured via the analysis of the subjects' saliva. It is estimated that cortisol is present in this material for about 10 minutes from the occurrence of the stress factor [81]. It was proved that, in 129 subjects, the levels of cortisol drop in accordance with the circadian rhythm. At the same time, the studies proved that self-inflicted injuries in children with ASD may be a stress-provoking factor in parents [57],[82].

    In order to reduce parental stress, parents of ASD children are recommended to introduce effective mitigation of autism symptoms [83]. It is emphasized that only successful reduction of ASD symptoms in the child may improve the well-being of the whole family [84]. Long-term stress may have drastic health consequences on parents of ASD children [31]. Support groups for parents of ASD children are one of the forms of therapy aimed at coping with stress and preventing burn-out [85].

    (1). It has been established that the parents of children with autism achieve lower results in the balanced cohesion sub-scale than the control group.

    (2). The parents of ASD children obtained higher scores in the disengaged sub-scale than the control group.

    (3). Furthermore, in the unmeshed sub-scale, their scores were higher than in the control group.

    (4). In the chaotic sub-scale, the parents of ASD children obtained higher scores than the control group.

    (5). It was found out that the family satisfaction level in parents of ASD children is lower than in the control group.

    (6). In the balanced flexibility, rigid and family communication sub-scales, there were no statistically significant differences between the parents of ASD children and the parents from the control group.

    (7). In parents of ASD children, the scores in all unbalanced sub-scales were higher than in families with children without autism (even if in some of differences were not statistically significant) while the scores in the balanced sub-scales were lower.

    (8). The STEN analysis of mean results of the parents of ASD children does not show extreme results in the scales studied, their results remain in the mid-range values (with the assumption that the middle of the STEN scale is 5.5 and the standard deviation is 2).

    (9). In families with ASD children, there is a higher risk of the unbalanced or rigidly disengaged family type than in families with neurotypical children.

    This may be a significant result, suggesting the risk of the occurrence of a disturbed family system, functioning in families with children with ASD, which should be a trigger for providing these families with early family functioning diagnosis and consequent support and therapy.


    Acknowledgments



    We would like to express our sincere gratitude to Prof. Cecilia Mascolo, clinical scientists at Cambridge University, for sharing the dataset. We acknowledge everyone who is trying to stop the COVID-19 pandemic.

    Author contributions



    All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. The authors have not been submitted this article too nor under review to any journal or publishing body.

    Conflict of interest



    The authors have declared no conflict of interest.

    [1] World Health Organization. Coronavirus disease 2019 (covid-19) (2020) .Available from: https://www.who.int/.
    [2] Wang Y, Hu M, Li Q, et al. (2020) Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner. arXiv:2002.05534 [cs.LG] .
    [3] Jiang Z, Hu M, Lei F, et al. (2020) Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections Such as Covid-19 on Portable Device. arXiv:2004.06912 [cs.CV] .
    [4] Imran A, Posokhova I, Qureshi HN, et al. (2020) AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Inform Med Unlocked 20: 100378. doi: 10.1016/j.imu.2020.100378
    [5] Shuja J, Alanazi E, Alasmary W, et al. (2020) COVID-19 open source data sets: a comprehensive survey. Appl Intell 21: 1-30.
    [6] Rasheed J, Jamil A, Hameed AA, et al. (2020) A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic. Chaos Solitons Fractals 141: 110337. doi: 10.1016/j.chaos.2020.110337
    [7] Alafif T, Tehame AM, Bajaba S, et al. (2021) Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions. Int J Environ Res Public Health 18: 1117. doi: 10.3390/ijerph18031117
    [8] Ritwik KVS, Shareef BK, Deepu V (2020) Covid-19 Patient Detection from Telephone Quality Speech Data. arXiv:2011.04299v1 [cs.SD] .
    [9] Kranthi KL, Alphonse PJA (2021) A literature review on COVID-19 disease diagnosis from respiratory sound data. AIMS Bioeng 8: 140-153. doi: 10.3934/bioeng.2021013
    [10] Huang Y, Meng S, Zhang Y, et al. (2020) The respiratory sound features of COVID-19 patients fill gaps between clinical data and screening methods. medRxiv 2020.04.07.20051060 .
    [11] Shi J, Zheng X, Li Y, et al. (2018) Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease. IEEE J Biomed Health Inform 22: 173-183. doi: 10.1109/JBHI.2017.2655720
    [12] Brabenec L, Mekyska J, Galaz Z, et al. (2017) Speech disorders in Parkinson's disease: early diagnostics and effects of medication and brain stimulation. J Neural Transm (Vienna) 124: 303-334. doi: 10.1007/s00702-017-1676-0
    [13] Erdogdu SB, Serbes G, Sakar CO (2017) Analyzing the effectiveness of vocal features in early telediagnosis of Parkinson's disease. PLoS ONE 12: e0182428. doi: 10.1371/journal.pone.0182428
    [14] Li F, Liu M, Zhao Y, et al. (2019) Feature extraction and classification of heart sound using 1D convolutional neural networks. EURASIP J Adv Signal Process 59.
    [15] Klára V, Viktor I, Krisztina M (2011) Voice Disorder Detection on the Basis of Continuous Speech. 5th European Conference of the International Federation for Medical and Biological Engineering Berlin, Heidelberg: IFMBE Proceedings, Springer.
    [16] Verde L, De Pietro D, Sannino G (2018) Voice Disorder Identification by Using Machine Learning Techniques. IEEE Access 6: 16246-16255. doi: 10.1109/ACCESS.2018.2816338
    [17] Bader M, Shahin I, Hassan A (2020) Studying the Similarity of COVID-19 Sounds based on Correlation Analysis of MFCC. arXiv:2010.08770 [cs.SD] .
    [18] Sahidullah Md, Saha G (2012) Design, analysis and experimental evaluation of block-based transformation in MFCC computation for speaker recognition. Speech Commun 54: 543-565. doi: 10.1016/j.specom.2011.11.004
    [19] Srinivasamurthy RS (2018) Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals. All Thesis. Available from: https://tigerprints.clemson.edu/cgi/viewcontent.cgi?article=3918&context=all_theses.
    [20] Zhao W, Singh R (2020) Speech-Based Parameter Estimation of an Asymmetric Vocal Fold Oscillation Model and its Application in Discriminating Vocal Fold Pathologies. ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Barcelona, Spain.
    [21] Kumar A, Gupta PK, Srivastava A (2020) A review of modern technologies for tackling COVID-19 pandemic. Diabetes Metab Syndr 14: 569-573. doi: 10.1016/j.dsx.2020.05.008
    [22] Deshpande G, Schuller B (2020) An Overview on Audio, Signal, Speech, & Language Processing for COVID-19. arXiv:2005.08579 [cs.CY] .
    [23] Brown C, Chauhan J, Grammenos A, et al. (2020) Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining .
    [24] Han J, Qian K, Song M, et al. (2020) An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety. arXiv:2005.00096v2 [eess.AS] .
    [25] Orlandic L, Teijeiro T, Atienza D (2020) The COUGHVID crowdsourcing dataset: A corpus for the study of large scale cough analysis algorithms. arXiv:2009.11644v1 [cs.SD] .
    [26] Singh R (2019) Production and Perception of Voice. Profiling Humans from their Voice Singapore: Springer. doi: 10.1007/978-981-13-8403-5
    [27] Hassan A, Shahin I, Alsabek MB (2020) COVID-19 Detection System using Recurrent Neural Networks. 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Sharjah, United Arab Emirates.
    [28] Chaudhari G, Jiang X, Fakhry A, et al. (2021) Virufy: Global Applicability of Crowdsourced and Clinical Datasets for AI Detection of COVID-19 from Cough. arXiv. PPR: PPR272849 .
    [29] Ismail MA, Deshmukh S, Rita S (2020) Detection of COVID-19 through the Analysis of Vocal Fold Oscillations. arXiv:2010.10707v1 [eess.AS] .
    [30] Laguarta J, Hueto F, Subirana B (2020) COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings. IEEE Open J Eng Med Biol 1: 275-281. doi: 10.1109/OJEMB.2020.3026928
    [31] Wang L, Lin ZQ, Wong A (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep 10: 19549. doi: 10.1038/s41598-020-76550-z
    [32] Quartieri TF, Talker T, Palmer JS (2020) A Framework for Biomarkers of COVID-19 Based on Coordination of Speech-Production Subsystems. IEEE Open J Eng Med Biol 1: 203-206. doi: 10.1109/OJEMB.2020.2998051
    [33] Sajjad A, Patrick C, Alessandro LK (2019) End-to-end environmental sound classification using a 1D convolutional neural network. Expert Sys Appl 136: 252-263. doi: 10.1016/j.eswa.2019.06.040
    [34] Li Y, Baidoo C, Cai T, et al. (2019) Speech Emotion Recognition Using 1D CNN with No Attention. 2019 23rd International Computer Science and Engineering Conference (ICSEC) Phuket, Thailand.
    [35] Serkan K, Onur A, Osama A, et al. (2019) 1D Convolutional Neural Networks and Applications: A Survey. Mech Sys Signal Proc 151: 107398.
    [36] Kiranyaz S, Ince T, Abdeljaber O, et al. (2019) 1-D Convolutional Neural Networks for Signal Processing Applications. ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Brighton, UK.
    [37] Salamon J, Bello JP (2017) Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification. IEEE Signal Proc Lett 24: 279-283. doi: 10.1109/LSP.2017.2657381
    [38] Aditya K, Deepak G, Nguyen NG, et al. (2019) Sound Classification Using Convolutional Neural Network and Tensor Deep Stacking Network. IEEE Access 7: 7717-7727. doi: 10.1109/ACCESS.2018.2888882
    [39] Chen X, Kopsaftopoulos F, Wu Q, et al. (2019) A Self-Adaptive 1D Convolutional Neural Network for Flight-State Identification. Sensors 19: 275. doi: 10.3390/s19020275
    [40] Pons J, Serra X (2019) Randomly Weighted CNNs for (Music) Audio Classification. ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Brighton, UK.
    [41] Aykanat M, Kılıç O, Kurt B, et al. (2017) Classification of lung sounds using convolutional neural networks. J Image Video Proc 65. doi: 10.1186/s13640-017-0213-2
    [42] Ismael AM, Abdulkadir S (2021) Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Sys Appl 164: 114054. doi: 10.1016/j.eswa.2020.114054
    [43] Minaee S, Abdolrashidi A, Su H, et al. (2021) Biometrics Recognition Using Deep Learning: A Survey. arXiv:1912.00271 [cs.CV] .
    [44] Yazdani S, Minaee S, Kafieh R, et al. (2020) COVID CT-Net: Predicting Covid-19 from Chest CT Images Using Attentional Convolutional Network. arXiv:2009.05096 [eess.IV] .
    [45] Jain R, Gupta M, Taneja S, et al. (2021) Deep learning-based detection and analysis of COVID-19 on chest X-ray images. Appl Intell 51: 1690-1700. doi: 10.1007/s10489-020-01902-1
    [46] Khan AI, Shah JL, Bhat MM (2020) CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest X-ray images. Comput Methods Programs Biomed 196: 105581. doi: 10.1016/j.cmpb.2020.105581
    [47] Wu Y, Yang F, Liu Y, et al. (2018) A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification. arXiv:1810.07088v1 [cs.CV] .
    [48] Ioffe S, Szegedy C (2015) Batch Normalization: Accelerating the deep network training by reducing internal covariate shift, Proceedings of the 32nd International Conference on Machine Learning. Proceed Mach Learn Res 37: 448-456.
  • This article has been cited by:

    1. Gwendoline DESQUENNE GODFREY, Naomi DOWNES, Emilie CAPPE, A Systematic Review of Family Functioning in Families of Children on the Autism Spectrum, 2023, 0162-3257, 10.1007/s10803-022-05830-6
    2. Emma Chad-Friedman, Karen A. Kuhlthau, Rachel A. Millstein, Giselle K. Perez, Christina M. Luberto, Lara Traeger, Jacqueline Proszynski, Elyse Park, Characteristics and Experiences of Parents of Children with Learning and Attention Disabilities and Autism Spectrum Disorder: A Mixed Methods Study, 2022, 30, 1066-4807, 427, 10.1177/10664807211052304
    3. Anna Kostiukow, Piotr Poniewierski, Dominika Janowska, Włodzimierz Samborski, Levels of happiness and depression in parents of children with autism spectrum disorder in Poland, 2021, 81, 0065-1400, 279, 10.21307/ane-2021-026
    4. Talal E. Alhuzimi, Family Functioning and Strengths in Families of Children With Autism Spectrum Disorder in Saudi Arabia, 2024, 32, 1066-4807, 230, 10.1177/10664807231217061
    5. Fátima El‐Bouhali‐Abdellaoui, Núria Voltas, Paula Morales‐Hidalgo, Josefa Canals, Examining the Relationship Between Parental Broader Autism Phenotype Traits, Offspring Autism, and Parental Mental Health, 2024, 1939-3792, 10.1002/aur.3295
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(6026) PDF downloads(434) Cited by(55)

Figures and Tables

Figures(10)  /  Tables(6)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog