Noise detection during heart sound recording using periodicity signatures

Kumar, Deepak; Carvalho, Paulo; Antunes, M.; Paiva, Rui Pedro; Henriques, J.

doi:10.1088/0967-3334/32/5/008

Cited by 53 publications

(22 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The external disturbances include a wide frequency and intensity spectrum of noise caused by speech and noise caused by motion, whereas the group of signals with internal origin disturbances consists of mainly signals caused by digestive and respiratory processes. Moreover, there are many other types of noise, which during the measurement may occur occasionally, such as vocal (coughing, laughing), physiological (muscle movements, swallowing), sensor (rubbing) and ambient (knocking at the door, ambient music, phone ringing, footsteps) [ 32 ]. Due to the existence of such noises, some components of the HS may become extremely hard to hear during auscultation—especially the murmurs which have lower amplitude and similar characteristics to noise.…”

Section: Literature Articles Review Resultsmentioning

confidence: 99%

The electronic stethoscope

et al. 2015

View full text Add to dashboard Cite

Most heart diseases are associated with and reflected by the sounds that the heart produces. Heart auscultation, defined as listening to the heart sound, has been a very important method for the early diagnosis of cardiac dysfunction. Traditional auscultation requires substantial clinical experience and good listening skills. The emergence of the electronic stethoscope has paved the way for a new field of computer-aided auscultation. This article provides an in-depth study of (1) the electronic stethoscope technology, and (2) the methodology for diagnosis of cardiac disorders based on computer-aided auscultation. The paper is based on a comprehensive review of (1) literature articles, (2) market (state-of-the-art) products, and (3) smartphone stethoscope apps. It covers in depth every key component of the computer-aided system with electronic stethoscope, from sensor design, front-end circuitry, denoising algorithm, heart sound segmentation, to the final machine learning techniques. Our intent is to provide an informative and illustrative presentation of the electronic stethoscope, which is valuable and beneficial to academics, researchers and engineers in the technical field, as well as to medical professionals to facilitate its use clinically. The paper provides the technological and medical basis for the development and commercialization of a real-time integrated heart sound detection, acquisition and quantification system.

show abstract

Section: Literature Articles Review Resultsmentioning

confidence: 99%

The electronic stethoscope

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Shannon energy in the frequency domain which we compute as in [13], accentuates the pressure differences found across heart valves, which leads to distinct frequency signatures of the valve closing sounds.…”

Section: B Features Extractionmentioning

confidence: 99%

Why should you model time when you use Markov models for heart sound analysis

Oliveira

Mantadelis

Coimbra

2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

Auscultation is a widely used technique in clinical activity to diagnose heart diseases. However, heart sounds are difficult to interpret because a) of events with very short temporal onset between them (tens of milliseconds) and b) dominant frequencies that are out of the human audible spectrum. In this paper, we propose a model to segment heart sounds using a semi-hidden Markov model instead of a hidden Markov model. Our model in difference from the state-of-the-art hidden Markov models takes in account the temporal constraints that exist in heart cycles. We experimentally confirm that semi-hidden Markov models are able to recreate the "true" continuous state sequence more accurately than hidden Markov models. We achieved a mean error rate per sample of 0.23.

show abstract

“…We assume S t to follow a hidden Markov chain with transition matrix Z = [z ij ], 1 ≤ i, j ≤ K where z ij = P (S t = j|S t−1 = i) denotes the probability of transition from state i at time t − 1 to state j at t. Each cardiac cycle of heart sound consists of four fundamental components: S1 sound; systolic interval (Sys); S2 sound; and diastolic interval (Dia). The heart sound components exhibit distinct dynamic patterns during different time periods, where each can be modeled as a piecewisestationary AR process of the MSAR model (2). Thus, we assume the number of states or regimes as K = 4 each corresponding to one of the four components (j = 1: S1, j = 2: Sys, j = 3: S2 and j = 4: Dia).…”

Section: Markov-switching Autoregression (Msar)mentioning

confidence: 99%

“…This is relatively simple in noise-free recordings. However, in clinical environments, this is difficult due to both endogenous or exogenous in-band noise sources that overlap with the heart sounds frequency range [2]. Accurate localization of the fundamental heart sounds will lead to a more accurate classification of any pathology in systolic or diastolic regions [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Markov-Switching Model Approach to Heart Sound Segmentation and Classification

Noman

Salleh

Ting

et al. 2020

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

Objective: This paper considers challenges in developing algorithms for accurate segmentation and classification of heart sound (HS) signals. Methods: We propose an approach based on Markov switching autoregressive model (MSAR) to segmenting the HS into four fundamental components each with distinct second-order structure. The identified boundaries are then utilized for automated classification of pathological HS using the continuous density hidden Markov model (CD-HMM). The MSAR formulated in a state-space form is able to capture simultaneously both the continuous hidden dynamics in HS, and the regime switching in the dynamics using a discrete Markov chain. This overcomes the limitation of HMM which uses a single-layer of discrete states. We introduce three schemes for model estimation: (1.) switching Kalman filter (SKF); (2.) refined SKF; (3.) fusion of SKF and the duration-dependent Viterbi algorithm (SKF-Viterbi). Results: The proposed methods are evaluated on Physionet/CinC Challenge 2016 database. The SKF-Viterbi significantly outperforms SKF by improvement of segmentation accuracy from 71% to 84.2%. The use of CD-HMM as a classifier and Mel-frequency cepstral coefficients (MFCCs) as features can characterize not only the normal and abnormal morphologies of HS signals but also morphologies considered as unclassifiable (denoted as X-Factor). It gives classification rates with best gross F 1 score of 90.19 (without X-Factor) and 82.7 (with X-Factor) for abnormal beats. Conclusion: The proposed MSAR approach for automatic localization and detection of pathological HS shows a noticeable performance on large HS dataset. Significance: It has potential applications in heart monitoring systems to assist cardiologists for pre-screening of heart pathologies.

show abstract

Noise detection during heart sound recording using periodicity signatures

Cited by 53 publications

References 22 publications

The electronic stethoscope

The electronic stethoscope

Why should you model time when you use Markov models for heart sound analysis

A Markov-Switching Model Approach to Heart Sound Segmentation and Classification

Contact Info

Product

Resources

About