Heart Rate Detection and Classification from Speech Spectral Features Using Machine Learning
Measurement of vital signs of the human body such as heart rate, blood pressure, body temperature and respiratory rate is an important part of diagnosing medical conditions and these are usually measured using medical equipment. In this paper, we propose to estimate an important vital sign – heart r...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Fundamental Technological Research Polish Academy of Sciences
2021-03-01
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| Series: | Archives of Acoustics |
| Subjects: | |
| Online Access: | https://acoustics.ippt.pan.pl/index.php/aa/article/view/2829 |
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| _version_ | 1849397230942814208 |
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| author | Mohammed USMAN Mohammed ZUBAIR Zeeshan AHMAD Monji ZAIDI Thafasal IJYAS Muneer PARAYANGAT Mohd WAJID Mohammad SHIBLEE Syed Jaffar ALI |
| author_facet | Mohammed USMAN Mohammed ZUBAIR Zeeshan AHMAD Monji ZAIDI Thafasal IJYAS Muneer PARAYANGAT Mohd WAJID Mohammad SHIBLEE Syed Jaffar ALI |
| author_sort | Mohammed USMAN |
| collection | DOAJ |
| description | Measurement of vital signs of the human body such as heart rate, blood pressure, body temperature and respiratory rate is an important part of diagnosing medical conditions and these are usually measured using medical equipment. In this paper, we propose to estimate an important vital sign – heart rate from speech signals using machine learning algorithms. Existing literature, observation and experience suggest the existence of a correlation between speech characteristics and physiological, psychological as well as emotional conditions. In this work, we estimate the heart rate of individuals by applying machine learning based regression algorithms to Mel frequency cepstrum coefficients, which represent speech features in the spectral domain as well as the temporal variation of spectral features. The estimated heart rate is compared with actual measurement made using a conventional medical device at the time of recording speech. We obtain estimation accuracy close to 94% between the estimated and actual measured heart rate values. Binary classification of heart rate as ‘normal’ or ‘abnormal’ is also achieved with 100% accuracy. A comparison of machine learning algorithms in terms of heart rate estimation and classification accuracy is also presented. Heart rate measurement using speech has applications in remote monitoring of patients, professional athletes and can facilitate telemedicine. |
| format | Article |
| id | doaj-art-bc0179363c2941e79d9b2ab343f64f19 |
| institution | Kabale University |
| issn | 0137-5075 2300-262X |
| language | English |
| publishDate | 2021-03-01 |
| publisher | Institute of Fundamental Technological Research Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Acoustics |
| spelling | doaj-art-bc0179363c2941e79d9b2ab343f64f192025-08-20T03:39:05ZengInstitute of Fundamental Technological Research Polish Academy of SciencesArchives of Acoustics0137-50752300-262X2021-03-0146110.24425/aoa.2021.136559Heart Rate Detection and Classification from Speech Spectral Features Using Machine LearningMohammed USMAN0Mohammed ZUBAIR1Zeeshan AHMAD2Monji ZAIDI3Thafasal IJYAS4Muneer PARAYANGAT5Mohd WAJID6Mohammad SHIBLEE7Syed Jaffar ALI8King Khalid UniversityKing Khalid UniversityKing Khalid UniversityKing Khalid UniversityKing Khalid UniversityKing Khalid UniversityAligarh Muslim UniversityTaif UniversityKing Khalid UniversityMeasurement of vital signs of the human body such as heart rate, blood pressure, body temperature and respiratory rate is an important part of diagnosing medical conditions and these are usually measured using medical equipment. In this paper, we propose to estimate an important vital sign – heart rate from speech signals using machine learning algorithms. Existing literature, observation and experience suggest the existence of a correlation between speech characteristics and physiological, psychological as well as emotional conditions. In this work, we estimate the heart rate of individuals by applying machine learning based regression algorithms to Mel frequency cepstrum coefficients, which represent speech features in the spectral domain as well as the temporal variation of spectral features. The estimated heart rate is compared with actual measurement made using a conventional medical device at the time of recording speech. We obtain estimation accuracy close to 94% between the estimated and actual measured heart rate values. Binary classification of heart rate as ‘normal’ or ‘abnormal’ is also achieved with 100% accuracy. A comparison of machine learning algorithms in terms of heart rate estimation and classification accuracy is also presented. Heart rate measurement using speech has applications in remote monitoring of patients, professional athletes and can facilitate telemedicine.https://acoustics.ippt.pan.pl/index.php/aa/article/view/2829heart rate from speechmachine learningMFCCregression and classificationspeech as a biomedical signal |
| spellingShingle | Mohammed USMAN Mohammed ZUBAIR Zeeshan AHMAD Monji ZAIDI Thafasal IJYAS Muneer PARAYANGAT Mohd WAJID Mohammad SHIBLEE Syed Jaffar ALI Heart Rate Detection and Classification from Speech Spectral Features Using Machine Learning Archives of Acoustics heart rate from speech machine learning MFCC regression and classification speech as a biomedical signal |
| title | Heart Rate Detection and Classification from Speech Spectral Features Using Machine Learning |
| title_full | Heart Rate Detection and Classification from Speech Spectral Features Using Machine Learning |
| title_fullStr | Heart Rate Detection and Classification from Speech Spectral Features Using Machine Learning |
| title_full_unstemmed | Heart Rate Detection and Classification from Speech Spectral Features Using Machine Learning |
| title_short | Heart Rate Detection and Classification from Speech Spectral Features Using Machine Learning |
| title_sort | heart rate detection and classification from speech spectral features using machine learning |
| topic | heart rate from speech machine learning MFCC regression and classification speech as a biomedical signal |
| url | https://acoustics.ippt.pan.pl/index.php/aa/article/view/2829 |
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