Application of Morlet transform wavelet in the detection of paradoxical splitting of the second heart sound

Mgdob, H.M.; Torry, J.N.; Vincent, R.; Al-Naami, Bassam

doi:10.1109/cic.2003.1291156

Cited by 10 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The analyzing wavelet Ψ(n) has two important properties, that is, it is limited in time and has zero mean [ 41 ]. The choice of the analyzing wavelet depends on the analysis being performed; for this case, a Morlet wavelet was used because of its similarity to the BCG waveform and its wide use in biomedical analysis [ 42 - 45 ].…”

Section: Methodsmentioning

confidence: 99%

Zero-Effort Ambient Heart Rate Monitoring Using Ballistocardiography Detected Through a Seat Cushion: Prototype Development and Preliminary Study

Malik¹,

Boger²

2021

JMIR Rehabil Assist Technol

View full text Add to dashboard Cite

Background Cardiovascular diseases are a leading cause of death worldwide and result in significant economic costs to health care systems. The prevalence of cardiovascular conditions that require monitoring is expected to increase as the average age of the global population continues to rise. Although an accurate cardiac assessment can be performed at medical centers, frequent visits for assessment are not feasible for most people, especially those with limited mobility. Monitoring of vital signs at home is becoming an increasingly desirable, accessible, and practical alternative. As wearable devices are not the ideal solution for everyone, it is necessary to develop parallel and complementary approaches. Objective This research aims to develop a zero-effort, unobtrusive, cost-effective, and portable option for home-based ambient heart rate monitoring. Methods The prototype seat cushion uses load cells to acquire a user’s ballistocardiogram (BCG). The analog signal from the load cells is amplified and filtered by a signal-conditioning circuit before being digitally recorded. A pilot study with 20 participants was conducted to analyze the prototype’s ability to capture the BCG during five real-world tasks: sitting still, watching a video on a computer screen, reading, using a computer, and having a conversation. A novel algorithm based on the continuous wavelet transform was developed to extract the heart rate by detecting the largest amplitude values (J-peaks) in the BCG signal. Results The pilot study data showed that the BCG signals from all five tasks had sufficiently large portions to extract heart rate. The continuous wavelet transform–based algorithm for J-peak detection demonstrated an overall accuracy of 91.4% compared with electrocardiography. Excluding three outliers that had significantly noisy BCG data, the algorithm achieved 94.6% accuracy, which was aligned with that of wearable devices. Conclusions This study suggests that BCG acquired through a seat cushion is a viable alternative to wearable technologies. The prototype seat cushion presented in this study is an example of a relatively accessible, affordable, portable, and unobtrusive zero-effort approach to achieve frequent home-based ambient heart rate monitoring.

show abstract

Section: Methodsmentioning

confidence: 99%

Zero-Effort Ambient Heart Rate Monitoring Using Ballistocardiography Detected Through a Seat Cushion: Prototype Development and Preliminary Study

Malik¹,

Boger²

2021

JMIR Rehabil Assist Technol

View full text Add to dashboard Cite

show abstract

“…Many types of continuous wavelets exist, however, the wavelet that has produced the best results for identification so far has been the Morlet wavelet [4,6] defined by the mother wavelet…”

Section: Biometric Identification Of Cardiosynchronous Waveforms Utilmentioning

confidence: 99%

Biometric identification of cardiosynchronous waveforms utilizing person specific continuous and discrete wavelet transform features

Bhagavatula

Venugopalan

Blue³

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

In this paper we explore how a Radio Frequency Impedance Interrogation (RFII) signal may be used as a biometric feature. This could allow the identification of subjects in operational and potentially hostile environments. Features extracted from the continuous and discrete wavelet decompositions of the signal are investigated for biometric identification. In the former case, the most discriminative features in the wavelet space were extracted using a Fisher ratio metric. Comparisons in the wavelet space were done using the Euclidean distance measure. In the latter case, the signal was decomposed at various levels using different wavelet bases, in order to extract both low frequency and high frequency components. Comparisons at each decomposition level were performed using the same distance measure as before. The data set used consists of four subjects, each with a 15 minute RFII recording. The various data samples for our experiments, corresponding to a single heart beat duration, were extracted from these recordings. We achieve identification rates of up to 99% using the CWT approach and rates of up to 100% using the DWT approach. While the small size of the dataset limits the interpretation of these results, further work with larger datasets is expected to develop better algorithms for subject identification.

show abstract

“…where 𝛾(𝑠, 𝜏) is the continuous wavelet transform; 𝑓(𝑡) is the original signal that is decomposed into a set of basis functions s; 𝜓(𝑡) generated from a mother wavelet; * denotes the complex conjugate; and 𝑠 and 𝜏 denote the scaling and translation of the mother wavelet, respectively. The Morlet wavelet was used [37] due to its similarity to the fundamental heart sounds [38,39] and its ability to provide a reliable time-frequency representation for PCG signal analysis [40].…”

Section: ) Continuous Wavelet Transformmentioning

confidence: 99%

Heart Sound Analysis in Individuals Supported With Left Ventricular Assist Devices

Chen

LaPorte

Olsen

et al. 2021

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

Left ventricular assist devices (LVADs) are surgically implanted mechanical pumps that improve survival rates for individuals with advanced heart failure. While life-saving, LVAD therapy is also associated with high morbidity, which can be partially attributed to the difficulties in identifying an LVAD complication before an adverse event occurs. Methods that are currently used to monitor for complications in LVAD-supported individuals require frequent clinical assessments at specialized LVAD centers. Remote analysis of digitally recorded precordial sounds has the potential to provide an inexpensive point-of-care diagnostic tool to assess both device function and the degree of cardiac support in LVAD recipients, facilitating real-time, remote monitoring for early detection of complications. To our knowledge, prior studies of precordial sounds in LVAD-supported individuals have analyzed LVAD noise rather than intrinsic heart sounds, due to a focus on detecting pump complications, and perhaps the obscuring of heart sounds by LVAD noise. In this letter, we describe an adaptive filtering method to remove sounds generated by the LVAD, making it possible to automatically isolate and analyze underlying heart sounds. We present preliminary results describing acoustic signatures of heart sounds extracted from in vivo data obtained from LVAD-supported individuals. These findings are significant as they provide proof-of-concept evidence for further exploration of heart sound analysis in LVADsupported individuals to identify cardiac abnormalities and changes in LVAD support.

show abstract

Application of Morlet transform wavelet in the detection of paradoxical splitting of the second heart sound

Abstract: Abstract

Cited by 10 publications

References 6 publications

Zero-Effort Ambient Heart Rate Monitoring Using Ballistocardiography Detected Through a Seat Cushion: Prototype Development and Preliminary Study

Zero-Effort Ambient Heart Rate Monitoring Using Ballistocardiography Detected Through a Seat Cushion: Prototype Development and Preliminary Study

Biometric identification of cardiosynchronous waveforms utilizing person specific continuous and discrete wavelet transform features

Heart Sound Analysis in Individuals Supported With Left Ventricular Assist Devices

Contact Info

Product

Resources

About