Method of Heart Sound Recognition Based on Wavelet Packet and BP Network

In order to extract pathological features of heart sound signal accurately, an algorithm for extracting the sub-band energy is developed based on the wavelet packet. The db6 wavelet is taken as the mother function, and the best wavelet packet basis of heart sound signal is picked out. Then, various heart sound signals are decomposed into four levels and the wavelet packet coefficients of the best basis are obtained. According to the equal-value relation between wavelet packet coefficients and signal energy in time domain, the normalized sub-band energy of the best basis is extracted as the feature vector. Based on BP network, seven identification models for seven kinds of heart sound were trained separately. Then, these models were tested by using 70 heart sounds, and the mean of identification accuracy is 72.9%.

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Heart Sound Abnormality Detection using Wavelet Packet Features and Machine Learning

10.1109/iria53009.2021.9588724 ◽

2021 ◽

Author(s):

Rohith Sai V ◽

Biswajit Karan ◽

Garima Thakur ◽

Ashutosh Rath ◽

Sitanshu Sekhar Sahu

Keyword(s):

Machine Learning ◽

Heart Sound ◽

Wavelet Packet ◽

Abnormality Detection

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Retention Of Cardiac Auscultation Skill Requires Paired Visual And Audio Information In New Learners

Journal of Medical Devices ◽

10.1115/1.2927390 ◽

2008 ◽

Vol 2 (2) ◽

Author(s):

Glenn Nordehn ◽

Spencer Strunic ◽

Tom Soldner ◽

Nicholas Karlisch ◽

Ian Kramer ◽

...

Keyword(s):

Visual Information ◽

Heart Sound ◽

Heart Sounds ◽

Sound Recognition ◽

Cardiac Auscultation ◽

Time Period ◽

Significant Difference ◽

Post Test ◽

Short Time ◽

Audio Information

Introduction: Cardiac auscultation accuracy is poor: 20% to 40%. Audio-only of 500 heart sounds cycles over a short time period significantly improved auscultation scores. Hypothesis: adding visual information to an audio-only format, significantly (p<.05) improves short and long term accuracy. Methods: Pre-test: Twenty-two 1st and 2nd year medical student participants took an audio-only pre-test. Seven students comprising our audio-only training cohort heard audio-only, of 500 heart sound repetitions. 15 students comprising our paired visual with audio cohort heard and simultaneously watched video spectrograms of the heart sounds. Immediately after trainings, both cohorts took audio-only post-tests; the visual with audio cohort also took a visual with audio post-test, a test providing audio with simultaneous video spectrograms. All tests were repeated in six months. Results: All tests given immediately after trainings showed significant improvement with no significant difference between the cohorts. Six months later neither cohorts maintained significant improvement on audio-only post-tests. Six months later the visual with audio cohort maintained significant improvement (p<.05) on the visual with audio post-test. Conclusions: Audio retention of heart sound recognition is not maintained if: trained using audio-only; or, trained using visual with audio. Providing visual with audio in training and testing allows retention of auscultation accuracy. Devices providing visual information during auscultation could prove beneficial.

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Research of portable heart sound recognition instrument based on embedded computer

Information Technology and Computer Application Engineering ◽

10.1201/b15936-177 ◽

2013 ◽

pp. 767-770

Keyword(s):

Heart Sound ◽

Sound Recognition ◽

Embedded Computer

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COMPARISON BETWEEN DISCRETE AND PACKET WAVELET TRANSFORM ANALYSES IN THE STUDY OF HEARTBEAT CARDIAC SOUNDS

Journal of Mechanics in Medicine and Biology ◽

10.1142/s021951940700225x ◽

2007 ◽

Vol 07 (02) ◽

pp. 199-214 ◽

Cited By ~ 2

Author(s):

S. M. DEBBAL ◽

F. BEREKSI-REGUIG

Keyword(s):

Wavelet Transform ◽

Heart Sound ◽

Wavelet Packet ◽

Extraction Methods ◽

Heart Sounds ◽

Discrete Wavelet ◽

Clinical Tool ◽

Prognostic Information ◽

Transform Method ◽

Time Frequency

This work investigates the study of heartbeat cardiac sounds through time–frequency analysis by using the wavelet transform method. Heart sounds can be utilized more efficiently by medical doctors when they are displayed visually rather through a conventional stethoscope. Heart sounds provide clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as a clinical tool, heart sound signals are so complex and nonstationary that they are very difficult to analyze in the time or frequency domain. We have studied the extraction of features from heart sounds in the time–frequency (TF) domain for the recognition of heart sounds through TF analysis. The application of wavelet transform (WT) for heart sounds is thus described. The performances of discrete wavelet transform (DWT) and wavelet packet transform (WP) are discussed in this paper. After these transformations, we can compare normal and abnormal heart sounds to verify the clinical usefulness of our extraction methods for the recognition of heart sounds.

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