Multiresolution analysis of heart sounds

El-Asir, B.; Khadra, L.; Al-Abbasi, A. H.; Mohammed, M.

doi:10.1109/icecs.1996.584609

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…This offers little advantage, because the duration of systolic murmurs changes across patients and cardiac abnormalities. A large number of time-frequency (White et al 1997, Asir et al 1996a, Shino et al 1997, Haghighi-Mood and Torry 1997, Kim and Tavel 2003 and wavelet (Tovar-Corona and Torry 1998, Asir et al 1996b, Liang and Hartimo 1998, Reed et al 2004 based techniques, which have time/frequency resolution problems, have been proposed to detect and analyze heart murmurs. However, no generic algorithm has been proposed to facilitate their extraction from the PCG.…”

Section: Murmur Type Physical Characteristicsmentioning

confidence: 99%

A simplicity-based fuzzy clustering approach for detection and extraction of murmurs from the phonocardiogram

Nigam

Priemer

2007

Physiol. Meas.

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Almost all cardiac abnormalities manifest themselves as murmurs in a phonocardiogram (PCG). The location of a murmur in the PCG over a cardiac cycle depends on the underlying cardiac abnormality. Locating murmurs with respect to the cardiac cycle is useful for diagnosing cardiac dysfunction. Locating murmurs is difficult due to spectral similarity and time overlap with other heart sounds. Moreover, the wide variation in murmur amplitudes and murmur spectral characteristics across different patients and abnormalities has hindered the design of a generic time segmentation algorithm to isolate murmurs within the PCG. In this paper, we present a method to locate cardiac murmurs within the PCG that is based on their visual simplicity, which does not depend upon their absolute amplitude and frequency characteristics, and hence, results in their better localization. Then, we identify fuzzy sets to cluster simplicity values, due to murmurs, to determine the time duration over which the murmur occurs. The overall accuracy of the proposed algorithm in detecting systolic murmurs is 80%. The sensitivity of the algorithm in locating systolic murmurs is 73% and its specificity is 100%. The isolated murmur can then be further processed to extract clinically relevant features to diagnose the nature of the cardiac abnormality. Experimental results with a variety of murmurs are provided.

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Section: Murmur Type Physical Characteristicsmentioning

confidence: 99%

A simplicity-based fuzzy clustering approach for detection and extraction of murmurs from the phonocardiogram

Nigam

Priemer

2007

Physiol. Meas.

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“…The heart sounds are multi-component, nonstationary signals (El-Asir and Mayyas 2004, Wood and Barry 1995, Xu et al 2001). …”

Section: Acoustic Monitor For Cardiac Soundsmentioning

confidence: 99%

Reduction of Skin Stretch Induced Motion Artifacts in Electrocardiogram Monitoring Using Adaptive Filtering

Liu

Pecht

2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

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Cardiovascular disease (CVD) is the leading cause of death in many regionsworldwide, accounting for nearly one third of global deaths in 2001. Wearable electrocardiographic cardiovascular monitoring devices have contributed to reduce CVD mortality and cost by enabling the diagnosis of conditions with infrequent symptoms, the timely detection of critical signs that can be precursor to sudden cardiac death, and the long-term assessment/monitoring of symptoms, risk factors, and the effects of therapy. However, the effectiveness of ambulatory electrocardiography to improve the treatment of CVD can be significantly impaired by motion artifacts which can cause misdiagnoses, inappropriate treatment decisions, and trigger false alarms. Skin stretch associated with patient motion is a main source of motion artifact in current ECG monitors. A promising approach to reduce motion artifact is the use of adaptive filtering that utilizes a measured reference input correlated with the motion artifact to extract noise from the ECG signal. Previous attempts to apply adaptive filtering to electrocardiography have employed either electrode deformation or acceleration, body acceleration, or skin/electrode impedance as a reference input, and were not successful at reducing motion artifacts in a consistent and reproducible manner. This has been essentially attributed to the lack of correlation between the reference input selected and the induced noise.In this study, motion artifacts are adaptively filtered by using skin strain as the reference signal. Skin strain is measured non-invasively using a light emitting diode (LED) and an optical sensor incorporated in an ECG electrode. The optical strain sensor is calibrated on animal skin samples and finally in-vivo, in terms of sensitivity and measurement range. Skin stretch induced artifacts are extracted in-vivo using adaptive filters. The system and method are tested for different individuals and under various types of ambulatory conditions with the noise reduction performance quantified.

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“…The abrupt frequency changes, the complex and highly nonstationary nature of the heart sound signals make the heart sound signal analysis a tedious job. The FFT and wavelet approaches have been applied to this in various studies and the work of correlating the heart sounds with the heart defects has been done [5][6][7][8][9][10]. Some related works to this study are as follows: where T1 and T2 are the widths of the first sound S1 and the second sound S2, T11 is the time interval between two abutted S1, which indicates the heart beat rhythm condition and T12 is the time interval between S1 and S2, which is an indicator to express the heart valvular murmurs [14].…”

Section: Previous Workmentioning

confidence: 99%

Heart Sounds Classification using Feature Extraction of Phonocardiography Signal

Singh¹,

Cheema²

2013

IJCA

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The Phonocardiogram (PCG) signals contain very useful information about the condition of the heart. By analyzing these signals, early detection and diagnosis of heart diseases can be done. It is also very useful in the case of infants, where ECG recording and other techniques are difficult to implement. In this paper, a classification method is proposed to classify normal and abnormal heart sound signals having murmurs without getting into the cumbersome process of segmenting fundamental heart sounds (FHS) using Electrocardiogram (ECG) gating. The proposed algorithm can be easily implemented on latest electronic stethoscopes, and therefore the unnecessary ECG can be avoided. General TermsClassification algorithm.

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Multiresolution analysis of heart sounds

Cited by 7 publications

References 3 publications

A simplicity-based fuzzy clustering approach for detection and extraction of murmurs from the phonocardiogram

A simplicity-based fuzzy clustering approach for detection and extraction of murmurs from the phonocardiogram

Reduction of Skin Stretch Induced Motion Artifacts in Electrocardiogram Monitoring Using Adaptive Filtering

Heart Sounds Classification using Feature Extraction of Phonocardiography Signal

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