Discrete Wavelet Transforms-Based Analysis of Accelerometer Signals for Continuous Human Cardiac Monitoring
Measuring cardiac activity from the chest using an accelerometer is commonly referred to as seismocardiography. Unfortunately, it cannot provide clinically valid data because it is easily corrupted by motion artefacts. This paper proposes two methods to improve peak detection from noisy seismocardio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/24/12072 |
| _version_ | 1797506728365916160 |
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| author | Hany Ferdinando Eveliina Seppälä Teemu Myllylä |
| author_facet | Hany Ferdinando Eveliina Seppälä Teemu Myllylä |
| author_sort | Hany Ferdinando |
| collection | DOAJ |
| description | Measuring cardiac activity from the chest using an accelerometer is commonly referred to as seismocardiography. Unfortunately, it cannot provide clinically valid data because it is easily corrupted by motion artefacts. This paper proposes two methods to improve peak detection from noisy seismocardiography data. They rely on discrete wavelet transform analysis using either biorthogonal 3.9 or reverse biorthogonal 3.9. The first method involves slicing chest vibrations for each cardiac activity, and then detecting the peak location, whereas the other method aims at detecting the peak directly from chest vibrations without segmentation. Performance evaluations were conducted on signals recorded from small children and adults based on missing and additional peaks. Both algorithms showed a low error rate (15.4% and 2.1% for children/infants and adults, respectively) for signals obtained in resting state. The average error for sitting and breathing tasks (adults only) was 14.4%. In summary, the first algorithm proved more promising for further exploration. |
| first_indexed | 2024-03-10T04:36:51Z |
| format | Article |
| id | doaj.art-9e9675fddf1e4984a36b84f94c4649d5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:36:51Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9e9675fddf1e4984a36b84f94c4649d52023-11-23T03:42:48ZengMDPI AGApplied Sciences2076-34172021-12-0111241207210.3390/app112412072Discrete Wavelet Transforms-Based Analysis of Accelerometer Signals for Continuous Human Cardiac MonitoringHany Ferdinando0Eveliina Seppälä1Teemu Myllylä2Research Unit of Medical Imaging, Physics, and Technology, Oulu University, 90100 Oulu, FinlandResearch Unit of Medical Imaging, Physics, and Technology, Oulu University, 90100 Oulu, FinlandResearch Unit of Medical Imaging, Physics, and Technology, Oulu University, 90100 Oulu, FinlandMeasuring cardiac activity from the chest using an accelerometer is commonly referred to as seismocardiography. Unfortunately, it cannot provide clinically valid data because it is easily corrupted by motion artefacts. This paper proposes two methods to improve peak detection from noisy seismocardiography data. They rely on discrete wavelet transform analysis using either biorthogonal 3.9 or reverse biorthogonal 3.9. The first method involves slicing chest vibrations for each cardiac activity, and then detecting the peak location, whereas the other method aims at detecting the peak directly from chest vibrations without segmentation. Performance evaluations were conducted on signals recorded from small children and adults based on missing and additional peaks. Both algorithms showed a low error rate (15.4% and 2.1% for children/infants and adults, respectively) for signals obtained in resting state. The average error for sitting and breathing tasks (adults only) was 14.4%. In summary, the first algorithm proved more promising for further exploration.https://www.mdpi.com/2076-3417/11/24/12072accelerometersbiomedical signal processingdiscrete wavelet transformsseismocardiography (SCG)peak detection |
| spellingShingle | Hany Ferdinando Eveliina Seppälä Teemu Myllylä Discrete Wavelet Transforms-Based Analysis of Accelerometer Signals for Continuous Human Cardiac Monitoring Applied Sciences accelerometers biomedical signal processing discrete wavelet transforms seismocardiography (SCG) peak detection |
| title | Discrete Wavelet Transforms-Based Analysis of Accelerometer Signals for Continuous Human Cardiac Monitoring |
| title_full | Discrete Wavelet Transforms-Based Analysis of Accelerometer Signals for Continuous Human Cardiac Monitoring |
| title_fullStr | Discrete Wavelet Transforms-Based Analysis of Accelerometer Signals for Continuous Human Cardiac Monitoring |
| title_full_unstemmed | Discrete Wavelet Transforms-Based Analysis of Accelerometer Signals for Continuous Human Cardiac Monitoring |
| title_short | Discrete Wavelet Transforms-Based Analysis of Accelerometer Signals for Continuous Human Cardiac Monitoring |
| title_sort | discrete wavelet transforms based analysis of accelerometer signals for continuous human cardiac monitoring |
| topic | accelerometers biomedical signal processing discrete wavelet transforms seismocardiography (SCG) peak detection |
| url | https://www.mdpi.com/2076-3417/11/24/12072 |
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