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Wang Y, Wang H, Li Z, Zhang H, Yang L, Li J, Tang Z, Hou S, Wang Q. Sound as a bell: a deep learning approach for health status classification through speech acoustic biomarkers. Chin Med 2024; 19:101. [PMID: 39049005 PMCID: PMC11267751 DOI: 10.1186/s13020-024-00973-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Accepted: 07/16/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Human health is a complex, dynamic concept encompassing a spectrum of states influenced by genetic, environmental, physiological, and psychological factors. Traditional Chinese Medicine categorizes health into nine body constitutional types, each reflecting unique balances or imbalances in vital energies, influencing physical, mental, and emotional states. Advances in machine learning models offer promising avenues for diagnosing conditions like Alzheimer's, dementia, and respiratory diseases by analyzing speech patterns, enabling complementary non-invasive disease diagnosis. The study aims to use speech audio to identify subhealth populations characterized by unbalanced constitution types. METHODS Participants, aged 18-45, were selected from the Acoustic Study of Health. Audio recordings were collected using ATR2500X-USB microphones and Praat software. Exclusion criteria included recent illness, dental issues, and specific medical histories. The audio data were preprocessed to Mel-frequency cepstral coefficients (MFCCs) for model training. Three deep learning models-1-Dimensional Convolution Network (Conv1D), 2-Dimensional Convolution Network (Conv2D), and Long Short-Term Memory (LSTM)-were implemented using Python to classify health status. Saliency maps were generated to provide model explainability. RESULTS The study used 1,378 recordings from balanced (healthy) and 1,413 from unbalanced (subhealth) types. The Conv1D model achieved a training accuracy of 91.91% and validation accuracy of 84.19%. The Conv2D model had 96.19% training accuracy and 84.93% validation accuracy. The LSTM model showed 92.79% training accuracy and 87.13% validation accuracy, with early signs of overfitting. AUC scores were 0.92 and 0.94 (Conv1D), 0.99 (Conv2D), and 0.97 (LSTM). All models demonstrated robust performance, with Conv2D excelling in discrimination accuracy. CONCLUSIONS The deep learning classification of human speech audio for health status using body constitution types showed promising results with Conv1D, Conv2D, and LSTM models. Analysis of ROC curves, training accuracy, and validation accuracy showed all models robustly distinguished between balanced and unbalanced constitution types. Conv2D excelled with good accuracy, while Conv1D and LSTM also performed well, affirming their reliability. The study integrates constitution theory and deep learning technologies to classify subhealth populations using noninvasive approach, thereby promoting personalized medicine and early intervention strategies.
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Affiliation(s)
- Yanbing Wang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Haiyan Wang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Zhuoxuan Li
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Haoran Zhang
- School of Management, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Liwen Yang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Jiarui Li
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Zixiang Tang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Shujuan Hou
- National Institute of TCM Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China.
| | - Qi Wang
- National Institute of TCM Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China.
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Nyawornota VK, Adamba C, Tay DA, Nyanyofio OC, Muomah RC, Chukwuorji JC, Nwonyi SK, Malete L, Joachim D, Ocansey RT. Self-perception of health and physical activity levels among the youth and adults before and amidst the COVID-19 pandemic. Front Public Health 2024; 12:1298378. [PMID: 38873289 PMCID: PMC11169886 DOI: 10.3389/fpubh.2024.1298378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 04/22/2024] [Indexed: 06/15/2024] Open
Abstract
Background Emerging research indicates that the COVID-19 pandemic and associated restrictions led to decreased physical activity levels and poorer health globally. However, most studies on this topic have focused on advanced countries, leaving a gap in understanding the impact in countries like Ghana. This study aimed to fill this gap by assessing self-perceived health status and physical activity levels among youth and adults in Ghana before, during, and after the COVID-19 restrictions. Understanding these dynamics is crucial for informing public health interventions and policies to promote well-being during and beyond the pandemic. Methods A cross-sectional survey using online data collection methods was conducted, involving 937 participants. Data included demographic information, and International Physical Activity Questionnaire-Short Form (IPAQ-SF). Analysis was done using SPSS version 25, with descriptive statistics and multinomial regression. Results Most participants (89.6%) reported good health. Male participants were significantly more likely to engage in moderate (1.78 times) and high (3.17 times) physical activity during the COVID-19 period compared to females. Conclusion This study highlights gender disparities in physical activity levels during the pandemic in Ghana. Addressing these disparities and promoting healthier lifestyles, especially during crises, is crucial for general and mental health. Further research should explore socio-demographic factors' role in health behaviors during pandemics.
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Affiliation(s)
| | - Clement Adamba
- Department of Educational Studies and Leadership, University of Ghana, Accra, Ghana
| | - Doris Akosua Tay
- Regional Institute for Population Studies, University of Ghana, Accra, Ghana
| | - Oscar C. Nyanyofio
- Department of Physical Education and Sport Studies, University of Ghana, Accra, Ghana
| | - Rosemary C. Muomah
- Department of Psychological Medicine, University of Nigeria, Nsukka, Nigeria
| | - JohnBosco C. Chukwuorji
- Department of Educational Studies and Leadership, University of Ghana, Accra, Ghana
- Department of Psychology, University of Nigeria, Nsukka, Nigeria
| | - Sampson K. Nwonyi
- Department of Psychology, Ebonyi State University, Abakaliki, Nigeria
| | - Leapetswe Malete
- Department of Kinesiology, Michigan State University, East Lansing, MI, United States
| | | | - Reginald T. Ocansey
- Department of Physical Education and Sport Studies, University of Ghana, Accra, Ghana
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Kapetanidis P, Kalioras F, Tsakonas C, Tzamalis P, Kontogiannis G, Karamanidou T, Stavropoulos TG, Nikoletseas S. Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:1173. [PMID: 38400330 PMCID: PMC10893010 DOI: 10.3390/s24041173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/03/2024] [Accepted: 02/04/2024] [Indexed: 02/25/2024]
Abstract
Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases' symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems.
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Affiliation(s)
- Panagiotis Kapetanidis
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
| | - Fotios Kalioras
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
| | - Constantinos Tsakonas
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
| | - Pantelis Tzamalis
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
| | - George Kontogiannis
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
| | - Theodora Karamanidou
- Pfizer Center for Digital Innovation, 55535 Thessaloniki, Greece; (T.K.); (T.G.S.)
| | | | - Sotiris Nikoletseas
- Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece (C.T.); (G.K.); (S.N.)
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Alvarado E, Grágeda N, Luzanto A, Mahu R, Wuth J, Mendoza L, Stern RM, Yoma NB. Automatic Detection of Dyspnea in Real Human-Robot Interaction Scenarios. SENSORS (BASEL, SWITZERLAND) 2023; 23:7590. [PMID: 37688044 PMCID: PMC10490721 DOI: 10.3390/s23177590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/20/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023]
Abstract
A respiratory distress estimation technique for telephony previously proposed by the authors is adapted and evaluated in real static and dynamic HRI scenarios. The system is evaluated with a telephone dataset re-recorded using the robotic platform designed and implemented for this study. In addition, the original telephone training data are modified using an environmental model that incorporates natural robot-generated and external noise sources and reverberant effects using room impulse responses (RIRs). The results indicate that the average accuracy and AUC are just 0.4% less than those obtained with matched training/testing conditions with simulated data. Quite surprisingly, there is not much difference in accuracy and AUC between static and dynamic HRI conditions. Moreover, the beamforming methods delay-and-sum and MVDR lead to average improvement in accuracy and AUC equal to 8% and 2%, respectively, when applied to training and testing data. Regarding the complementarity of time-dependent and time-independent features, the combination of both types of classifiers provides the best joint accuracy and AUC score.
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Affiliation(s)
- Eduardo Alvarado
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
| | - Nicolás Grágeda
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
| | - Alejandro Luzanto
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
| | - Rodrigo Mahu
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
| | - Jorge Wuth
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
| | - Laura Mendoza
- Hospital Clínico Universidad de Chile, Santiago 8380420, Chile;
- Clínica Alemana, Santiago 7630000, Chile
| | - Richard M. Stern
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA;
| | - Néstor Becerra Yoma
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
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Utilizing CNN-LSTM techniques for the enhancement of medical systems. ALEXANDRIA ENGINEERING JOURNAL 2023; 72:323-338. [PMCID: PMC10105249 DOI: 10.1016/j.aej.2023.04.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/30/2023] [Accepted: 04/05/2023] [Indexed: 04/04/2024]
Abstract
COVID-19 is one of the most chronic and serious infections of recent years due to its worldwide spread. Determining who was genuinely affected when the disease spreads more widely is challenging. More than 60% of affected individuals report having a dry cough. In many recent studies, diagnostic models were developed using coughing and other breathing sounds. With the development of technology, body sounds are now collected using digital techniques for respiratory and cardiovascular tests. Early research on identifying COVID-19 utilizing speech and diagnosing signs yielded encouraging findings. The gathering of extensive, multi-group, airborne acoustical sound data is used in the developed framework to conduct an efficient assessment to test for COVID-19. An effective classification model is created to assess COVID-19 utilizing deep learning methods. The MIT-Covid-19 dataset is used as the input, and the Weiner filter is used for pre-processing. Following feature extraction done by Mel-frequency cepstral coefficients, the classification is performed using the CNN-LSTM approach. The study compared the performance of the developed framework with other techniques such as CNN, GRU, and LSTM. Study results revealed that CNN-LSTM outperformed other existing approaches by 97.7%.
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Diagnosis of COVID-19 from X-rays using combined CNN-RNN architecture with transfer learning. BENCHCOUNCIL TRANSACTIONS ON BENCHMARKS, STANDARDS AND EVALUATIONS 2023:100088. [PMCID: PMC10010001 DOI: 10.1016/j.tbench.2023.100088] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Combating the COVID-19 pandemic has emerged as one of the most promising issues in global healthcare. Accurate and fast diagnosis of COVID-19 cases is required for the right medical treatment to control this pandemic. Chest radiography imaging techniques are more effective than the reverse-transcription polymerase chain reaction (RT-PCR) method in detecting coronavirus. Due to the limited availability of medical images, transfer learning is better suited to classify patterns in medical images. This paper presents a combined architecture of convolutional neural network (CNN) and recurrent neural network (RNN) to diagnose COVID-19 patients from chest X-rays. The deep transfer techniques used in this experiment are VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2, where CNN is used to extract complex features from samples and classify them using RNN. In our experiments, the VGG19-RNN architecture outperformed all other networks in terms of accuracy. Finally, decision-making regions of images were visualized using gradient-weighted class activation mapping (Grad-CAM). The system achieved promising results compared to other existing systems and might be validated in the future when more samples would be available. The experiment demonstrated a good alternative method to diagnose COVID-19 for medical staff. All the data used during the study are openly available from the Mendeley data repository at https://data.mendeley.com/datasets/mxc6vb7svm. For further research, we have made the source code publicly available at https://github.com/Asraf047/COVID19-CNN-RNN.
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Ulukaya S, Sarıca AA, Erdem O, Karaali A. MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds. Med Biol Eng Comput 2023:10.1007/s11517-023-02803-4. [PMID: 36828944 PMCID: PMC9955529 DOI: 10.1007/s11517-023-02803-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 01/24/2023] [Indexed: 02/26/2023]
Abstract
Coronavirus has an impact on millions of lives and has been added to the important pandemics that continue to affect with its variants. Since it is transmitted through the respiratory tract, it has had significant effects on public health and social relations. Isolating people who are COVID positive can minimize the transmission, therefore several exams are proposed to detect the virus such as reverse transcription-polymerase chain reaction (RT-PCR), chest X-Ray, and computed tomography (CT). However, these methods suffer from either a low detection rate or high radiation dosage, along with being expensive. In this study, deep neural network-based model capable of detecting coronavirus from only coughing sound, which is fast, remotely operable and has no harmful side effects, has been proposed. The proposed multi-branch model takes M el Frequency Cepstral Coefficients (MFCC), S pectrogram, and C hromagram as inputs and is abbreviated as MSCCov19Net. The system is trained on publicly available crowdsourced datasets, and tested on two unseen (used only for testing) clinical and non-clinical datasets. Experimental outcomes represent that the proposed system outperforms the 6 popular deep learning architectures on four datasets by representing a better generalization ability. The proposed system has reached an accuracy of 61.5 % in Virufy and 90.4 % in NoCoCoDa for unseen test datasets.
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Affiliation(s)
- Sezer Ulukaya
- Department of Electrical and Electronics Engineering, Trakya University, Edirne, 22030 Turkey
| | - Ahmet Alp Sarıca
- Department of Electrical and Electronics Engineering, Trakya University, Edirne, 22030 Turkey
| | - Oğuzhan Erdem
- Department of Electrical and Electronics Engineering, Trakya University, Edirne, 22030 Turkey
| | - Ali Karaali
- The Irish Longitudinal Study on Ageing (TILDA), School of Medicine, Trinity College Dublin, Dublin, D02 R590 Ireland
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Alvarado E, Grágeda N, Luzanto A, Mahu R, Wuth J, Mendoza L, Yoma NB. Dyspnea Severity Assessment Based on Vocalization Behavior with Deep Learning on the Telephone. SENSORS (BASEL, SWITZERLAND) 2023; 23:2441. [PMID: 36904646 PMCID: PMC10007248 DOI: 10.3390/s23052441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/08/2023] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
In this paper, a system to assess dyspnea with the mMRC scale, on the phone, via deep learning, is proposed. The method is based on modeling the spontaneous behavior of subjects while pronouncing controlled phonetization. These vocalizations were designed, or chosen, to deal with the stationary noise suppression of cellular handsets, to provoke different rates of exhaled air, and to stimulate different levels of fluency. Time-independent and time-dependent engineered features were proposed and selected, and a k-fold scheme with double validation was adopted to select the models with the greatest potential for generalization. Moreover, score fusion methods were also investigated to optimize the complementarity of the controlled phonetizations and features that were engineered and selected. The results reported here were obtained from 104 participants, where 34 corresponded to healthy individuals and 70 were patients with respiratory conditions. The subjects' vocalizations were recorded with a telephone call (i.e., with an IVR server). The system provided an accuracy of 59% (i.e., estimating the correct mMRC), a root mean square error equal to 0.98, false positive rate of 6%, false negative rate of 11%, and an area under the ROC curve equal to 0.97. Finally, a prototype was developed and implemented, with an ASR-based automatic segmentation scheme, to estimate dyspnea on line.
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Affiliation(s)
- Eduardo Alvarado
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
| | - Nicolás Grágeda
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
| | - Alejandro Luzanto
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
| | - Rodrigo Mahu
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
| | - Jorge Wuth
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
| | - Laura Mendoza
- Clinical Hospital, University of Chile, Santiago 8380420, Chile
| | - Néstor Becerra Yoma
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
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Deepak G, Madiajagan M, Kulkarni S, Ahmed AN, Gopatoti A, Ammisetty V. MCSC-Net: COVID-19 detection using deep-Q-neural network classification with RFNN-based hybrid whale optimization. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:483-509. [PMID: 36872839 DOI: 10.3233/xst-221360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND COVID-19 is the most dangerous virus, and its accurate diagnosis saves lives and slows its spread. However, COVID-19 diagnosis takes time and requires trained professionals. Therefore, developing a deep learning (DL) model on low-radiated imaging modalities like chest X-rays (CXRs) is needed. OBJECTIVE The existing DL models failed to diagnose COVID-19 and other lung diseases accurately. This study implements a multi-class CXR segmentation and classification network (MCSC-Net) to detect COVID-19 using CXR images. METHODS Initially, a hybrid median bilateral filter (HMBF) is applied to CXR images to reduce image noise and enhance the COVID-19 infected regions. Then, a skip connection-based residual network-50 (SC-ResNet50) is used to segment (localize) COVID-19 regions. The features from CXRs are further extracted using a robust feature neural network (RFNN). Since the initial features contain joint COVID-19, normal, pneumonia bacterial, and viral properties, the conventional methods fail to separate the class of each disease-based feature. To extract the distinct features of each class, RFNN includes a disease-specific feature separate attention mechanism (DSFSAM). Furthermore, the hunting nature of the Hybrid whale optimization algorithm (HWOA) is used to select the best features in each class. Finally, the deep-Q-neural network (DQNN) classifies CXRs into multiple disease classes. RESULTS The proposed MCSC-Net shows the enhanced accuracy of 99.09% for 2-class, 99.16% for 3-class, and 99.25% for 4-class classification of CXR images compared to other state-of-art approaches. CONCLUSION The proposed MCSC-Net enables to conduct multi-class segmentation and classification tasks applying to CXR images with high accuracy. Thus, together with gold-standard clinical and laboratory tests, this new method is promising to be used in future clinical practice to evaluate patients.
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Affiliation(s)
- Gerard Deepak
- Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India
| | - M Madiajagan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Sanjeev Kulkarni
- Department of Information Science and Engineering, Yenepoya Institute of Technology, Mangalore, Karnataka, India
| | - Ahmed Najat Ahmed
- Department of Computer Engineering, Lebanese French University, Erbil, Iraq
| | - Anandbabu Gopatoti
- Department of Electronics and Communication Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India
| | - Veeraswamy Ammisetty
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India
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Silva G, Batista P, Rodrigues PM. COVID-19 activity screening by a smart-data-driven multi-band voice analysis. J Voice 2022:S0892-1997(22)00360-5. [PMID: 36464573 PMCID: PMC9663738 DOI: 10.1016/j.jvoice.2022.11.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 11/06/2022] [Accepted: 11/07/2022] [Indexed: 11/17/2022]
Abstract
COVID-19 is a disease caused by the new coronavirus SARS-COV-2 which can lead to severe respiratory infections. Since its first detection it caused more than six million worldwide deaths. COVID-19 diagnosis non-invasive and low-cost methods with faster and accurate results are still needed for a fast disease control. In this research, 3 different signal analyses have been applied (per broadband, per sub-bands and per broadband & sub-bands) to Cough, Breathing & Speech signals of Coswara dataset to extract non-linear patterns (Energy, Entropies, Correlation Dimension, Detrended Fluctuation Analysis, Lyapunov Exponent & Fractal Dimensions) for feeding a XGBoost classifier to discriminate COVID-19 activity on its different stages. Classification accuracies ranged between 83.33% and 98.46% have been achieved, surpassing the state-of-art methods in some comparisons. It should be empathized the 98.46% of accuracy reached on pair Healthy Controls vs all COVID-19 stages. The results shows that the method may be adequate for COVID-19 diagnosis screening assistance.
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Affiliation(s)
- Gabriel Silva
- CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua de Diogo Botelho, 1327, 4169-005 Porto, Portugal
| | - Patrícia Batista
- CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua de Diogo Botelho, 1327, 4169-005 Porto, Portugal; HNL/CEDH-Human Neurobehavioural Laboratory/Research Centre for Human Development, Universidade Católica Portuguesa, Rua de Diogo Botelho, 1327, 4169-005 Porto, Portugal
| | - Pedro Miguel Rodrigues
- CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua de Diogo Botelho, 1327, 4169-005 Porto, Portugal.
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Ngo QC, Motin MA, Pah ND, Drotár P, Kempster P, Kumar D. Computerized analysis of speech and voice for Parkinson's disease: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107133. [PMID: 36183641 DOI: 10.1016/j.cmpb.2022.107133] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 09/13/2022] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Speech impairment is an early symptom of Parkinson's disease (PD). This study has summarized the literature related to speech and voice in detecting PD and assessing its severity. METHODS A systematic review of the literature from 2010 to 2021 to investigate analysis methods and signal features. The keywords "Automatic analysis" in conjunction with "PD speech" or "PD voice" were used, and the PubMed and ScienceDirect databases were searched. A total of 838 papers were found on the first run, of which 189 were selected. One hundred and forty-seven were found to be suitable for the review. The different datasets, recording protocols, signal analysis methods and features that were reported are listed. Values of the features that separate PD patients from healthy controls were tabulated. Finally, the barriers that limit the wide use of computerized speech analysis are discussed. RESULTS Speech and voice may be valuable markers for PD. However, large differences between the datasets make it difficult to compare different studies. In addition, speech analytic methods that are not informed by physiological understanding may alienate clinicians. CONCLUSIONS The potential usefulness of speech and voice for the detection and assessment of PD is confirmed by evidence from the classification and correlation results.
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Affiliation(s)
| | - Mohammod Abdul Motin
- Biosignals Lab, RMIT University, Melbourne, Australia; Department of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Nemuel Daniel Pah
- Biosignals Lab, RMIT University, Melbourne, Australia; Universitas Surabaya, Indonesia
| | - Peter Drotár
- Intelligent Information Systems Lab, Technical University of Kosice, Letna 9, 42001, Kosice, Slovakia
| | - Peter Kempster
- Neurosciences Department, Monash Health, Clayton, VIC, Australia; Department of Medicine, School of Clinical Sciences, Monash University, Clayton, VIC, Australia
| | - Dinesh Kumar
- Biosignals Lab, RMIT University, Melbourne, Australia.
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Kumar S, Nagar R, Bhatnagar S, Vaddi R, Gupta SK, Rashid M, Bashir AK, Alkhalifah T. Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19. COMPUTERS & ELECTRICAL ENGINEERING : AN INTERNATIONAL JOURNAL 2022; 103:108391. [PMID: 36119394 PMCID: PMC9472671 DOI: 10.1016/j.compeleceng.2022.108391] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 09/05/2022] [Accepted: 09/09/2022] [Indexed: 05/27/2023]
Abstract
All witnessed the terrible effects of the COVID-19 pandemic on the health and work lives of the population across the world. It is hard to diagnose all infected people in real time since the conventional medical diagnosis of COVID-19 patients takes a couple of days for accurate diagnosis results. In this paper, a novel learning framework is proposed for the early diagnosis of COVID-19 patients using hybrid deep fusion learning models. The proposed framework performs early classification of patients based on collected samples of chest X-ray images and Coswara cough (sound) samples of possibly infected people. The captured cough samples are pre-processed using speech signal processing techniques and Mel frequency cepstral coefficient features are extracted using deep convolutional neural networks. Finally, the proposed system fuses extracted features to provide 98.70% and 82.7% based on Chest-X ray images and cough (audio) samples for early diagnosis using the weighted sum-rule fusion method.
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Affiliation(s)
- Santosh Kumar
- Department of Computer Science and Engineering, International Institute of Information Technology, Naya Raipur, Raipur, Chhattisgarh, 493661, India
| | - Rishab Nagar
- Department of Computer Science and Engineering, International Institute of Information Technology, Naya Raipur, Raipur, Chhattisgarh, 493661, India
| | - Saumya Bhatnagar
- Department of Computer Science and Engineering, International Institute of Information Technology, Naya Raipur, Raipur, Chhattisgarh, 493661, India
| | - Ramesh Vaddi
- Department of Electronics and Communication Engineering, School of Engineering and Applied Sciences, SRM University, Amaravati, Guntur, Andhra Pradesh, 522240, India
| | - Sachin Kumar Gupta
- School of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, Katra, India
| | - Mamoon Rashid
- Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune, India
- Vishwakarma University Research Center of Excellence for Health Informatics, Pune, India
| | - Ali Kashif Bashir
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK
| | - Tamim Alkhalifah
- Department of computer science, College of Science and Arts in Ar Rass, Qassim University, Saudi Arabia
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Deep learning and machine learning-based voice analysis for the detection of COVID-19: A proposal and comparison of architectures. Knowl Based Syst 2022; 253:109539. [PMID: 35915642 PMCID: PMC9328841 DOI: 10.1016/j.knosys.2022.109539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 06/18/2022] [Accepted: 07/22/2022] [Indexed: 11/21/2022]
Abstract
Alongside the currently used nasal swab testing, the COVID-19 pandemic situation would gain noticeable advantages from low-cost tests that are available at any-time, anywhere, at a large-scale, and with real time answers. A novel approach for COVID-19 assessment is adopted here, discriminating negative subjects versus positive or recovered subjects. The scope is to identify potential discriminating features, highlight mid and short-term effects of COVID on the voice and compare two custom algorithms. A pool of 310 subjects took part in the study; recordings were collected in a low-noise, controlled setting employing three different vocal tasks. Binary classifications followed, using two different custom algorithms. The first was based on the coupling of boosting and bagging, with an AdaBoost classifier using Random Forest learners. A feature selection process was employed for the training, identifying a subset of features acting as clinically relevant biomarkers. The other approach was centered on two custom CNN architectures applied to mel-Spectrograms, with a custom knowledge-based data augmentation. Performances, evaluated on an independent test set, were comparable: Adaboost and CNN differentiated COVID-19 positive from negative with accuracies of 100% and 95% respectively, and recovered from negative individuals with accuracies of 86.1% and 75% respectively. This study highlights the possibility to identify COVID-19 positive subjects, foreseeing a tool for on-site screening, while also considering recovered subjects and the effects of COVID-19 on the voice. The two proposed novel architectures allow for the identification of biomarkers and demonstrate the ongoing relevance of traditional ML versus deep learning in speech analysis.
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Alencar WLM, da Silva Arouche T, Neto AFG, de Castro Ramalho T, de Carvalho Júnior RN, de Jesus Chaves Neto AM. Interactions of Co, Cu, and non-metal phthalocyanines with external structures of SARS-CoV-2 using docking and molecular dynamics. Sci Rep 2022; 12:3316. [PMID: 35228662 PMCID: PMC8885651 DOI: 10.1038/s41598-022-07396-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 02/10/2022] [Indexed: 02/06/2023] Open
Abstract
The new coronavirus, SARS-CoV-2, caused the COVID-19 pandemic, characterized by its high rate of contamination, propagation capacity, and lethality rate. In this work, we approach the use of phthalocyanines as an inhibitor of SARS-CoV-2, as they present several interactive properties of the phthalocyanines (Pc) of Cobalt (CoPc), Copper (CuPc) and without a metal group (NoPc) can interact with SARS-CoV-2, showing potential be used as filtering by adsorption on paints on walls, masks, clothes, and air conditioning filters. Molecular modeling techniques through Molecular Docking and Molecular Dynamics were used, where the target was the external structures of the virus, but specifically the envelope protein, main protease, and Spike glycoprotein proteases. Using the g_MM-GBSA module and with it, the molecular docking studies show that the ligands have interaction characteristics capable of adsorbing the structures. Molecular dynamics provided information on the root-mean-square deviation of the atomic positions provided values between 1 and 2.5. The generalized Born implicit solvation model, Gibbs free energy, and solvent accessible surface area approach were used. Among the results obtained through molecular dynamics, it was noticed that interactions occur since Pc could bind to residues of the active site of macromolecules, demonstrating good interactions; in particular with CoPc. Molecular couplings and free energy showed that S-gly active site residues interacted strongly with phthalocyanines with values of - 182.443 kJ/mol (CoPc), 158.954 kJ/mol (CuPc), and - 129.963 kJ/mol (NoPc). The interactions of Pc's with SARS-CoV-2 may predict some promising candidates for antagonists to the virus, which if confirmed through experimental approaches, may contribute to resolving the global crisis of the COVID-19 pandemic.
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Affiliation(s)
- Wilson Luna Machado Alencar
- Laboratory of Preparation and Computation of Nanomaterials (LPCN), Federal University of Pará, C. P. 479, Belem, PA, 66075-110, Brazil
- Pos-Graduation Program in Engineering of Natural Resources of the Amazon, ITEC, Federal University of Pará, C. P. 2626, Belém, PA, 66050-540, Brazil
- Federal Institute of Pará (IFPA), C. P. BR 316, Km 61, Castanhal, PA, 68740-970, Brazil
| | - Tiago da Silva Arouche
- Laboratory of Preparation and Computation of Nanomaterials (LPCN), Federal University of Pará, C. P. 479, Belem, PA, 66075-110, Brazil
| | | | | | - Raul Nunes de Carvalho Júnior
- Pos-Graduation Program in Engineering of Natural Resources of the Amazon, ITEC, Federal University of Pará, C. P. 2626, Belém, PA, 66050-540, Brazil
- Pos-Graduation Program in Chemical Engineering, ITEC, Federal University of Pará, C. P. 479, Belém, PA, 66075-900, Brazil
| | - Antonio Maia de Jesus Chaves Neto
- Laboratory of Preparation and Computation of Nanomaterials (LPCN), Federal University of Pará, C. P. 479, Belem, PA, 66075-110, Brazil.
- Pos-Graduation Program in Engineering of Natural Resources of the Amazon, ITEC, Federal University of Pará, C. P. 2626, Belém, PA, 66050-540, Brazil.
- Pos-Graduation Program in Chemical Engineering, ITEC, Federal University of Pará, C. P. 479, Belém, PA, 66075-900, Brazil.
- National Professional Master's in Physics Teaching, Federal University of Pará, C. P. 479, Belém, PA, 66075-110, Brazil.
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