Classification of normal/abnormal PCG recordings using a time–frequency approach

Hazeri, Hanie; Zarjam, Pega; Azemi, Ghasem

doi:10.1007/s10470-021-01867-2

Cited by 11 publications

(5 citation statements)

References 32 publications

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“…The states of healthy and damaged blade of a quadcopter are studied based on signal processing in real-time decisionmaking process [42]. However, sophisticated techniques such as Continuous Wavelet Transform (CWT) [43,44] and Discrete Wavelet Transform (DWT) [45][46][47][48]…”

Section: Fault Diagnosis Strategies For Predictive Maintenance Ismentioning

confidence: 99%

Vibration Signal Processing for Multirotor UAVs Fault Diagnosis: Filtering or Multiresolution Analysis?

Al-Haddad,

Giernacki,

Shandookh

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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In the modern technological advancements, Unmanned Aerial Vehicles (UAVs) have emerged across diverse applications. As UAVs evolve, fault diagnosis witnessed great advancements, with signal processing methodologies taking center stage. This paper presents an assessment of vibration-based signal processing techniques, focusing on Kalman filtering (KF) and Discrete Wavelet Transform (DWT) multiresolution analysis. Experimental evaluation of healthy and faulty states in a quadcopter, using an accelerometer, are presented. The determination of the 1024 Hz sampling frequency is facilitated through finite element analysis of 20 mode shapes. KF exhibits commendable performance, successfully segregating faulty and healthy peaks within an acceptable range. While the six-level multi-decomposition unveils good explanations for fluctuations eluding KF. Ultimately, both KF and DWT showcase high-performance capabilities in fault diagnosis. However, DWT shows superior assessment precision, uncovering intricate details and facilitating a holistic understanding of fault-related characteristics.

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Section: Fault Diagnosis Strategies For Predictive Maintenance Ismentioning

confidence: 99%

Vibration Signal Processing for Multirotor UAVs Fault Diagnosis: Filtering or Multiresolution Analysis?

Al-Haddad,

Giernacki,

Shandookh

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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“…In [6], nonlinear dynamics of the PCG signal is used. The signal's first derivative is decomposed into a set of frequency subbands with TQWT method.…”

Section: Performance Evaluation and Comparisonmentioning

confidence: 99%

“…With the advancement of digital signal processing kits, digital stethoscopes are getting popularity to acquire PCG, which helps to treat, analyze and interpret valve related disorders. PCG based automated methods are taken into account for developing intelligent stethoscopes with first hand decision making facilities [6].…”

Section: Introductionmentioning

confidence: 99%

“…Different statistical features of tunable Q-wavelet transform (TQWT) are applied to least square-support vector machine (LS-SVM), where an OA of 94.01% is achieved [29]. The fast and adaptive multivariate empirical mode decomposition (FA-MVEMD) and TQWT are reported in [6] to decompose the PCG signal and its first derivative into frequency subbands. Shannon energy envelope is then applied to extract the characteristic envelope of the first two intrinsic mode functions (IMFs) and an OA of 98.48% is achieved after classifying PCG system dynamics for normal/abnormal classes with deterministic learning theory (DLT).…”

Section: Introductionmentioning

confidence: 99%

“…Approximately, 96% average F1 score is testified so far for HSMM based segmentation [32]. In [6], a time-frequency feature based method is proposed to classify heart sounds into normal and abnormal classes. From [33], only 944 recordings are taken to extract 18 time-frequency features, and are used as inputs to a binary SVM-based classifier, achieving 86% for 10-fold cross validations.…”

Section: Introductionmentioning

confidence: 99%

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Automated Heart Valve Disorder Detection Based on PDF Modeling of Formant Variation Pattern in PCG Signal

2022

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Heart valve disorder (HVD) analysis from heart sound is being well known for a long period of time, and use of digital stethoscope gives opportunity to diagnose HVDs from phonocardiographic (PCG) signal. An automated HVD detection technique from PCG signal can play a key role as a firsthand diagnostic tool for the physicians. In this paper, in order to classify different HVDs, we propose to utilize the formant characteristic of the PCG signal, which is an acoustic property of the heart sound. PCG signals exhibit significant variations depending on different types of HVDs and thus conventional time frequency domain features or statistical features are extracted from PCG signal for disease classification. However, direct PCG signals are also used in sequential networks to classify HVDs. Similar to the formant peaks of voiced speech signal, the spectrum corresponding to the PCG signal exhibits distinguishable peaks, especially in the voiced part of the heart sound (lub-dub). Keeping this notable key point in consideration, Burg's autoregressive model is used to find the parametric spectrum of the PCG signal. The first two formants of the PCG signal, that carry the most informative acoustic properties of the heart sound, are estimated from the Burg's spectrum, and are used for feature extraction. The magnitude, frequency and phase of each formant are considered to evaluate these features. Instead of considering a long duration of PCG signal at a time, we consider the overlapping sub-frames, and extract formants from each subframe, which generates a temporal variation of the formants. Finally, we propose a PDF model fitting of the formant variation, and utilize the estimated model parameters along with some statistical features to classify the HVDs. Two famous publicly available PCG datasets are used to demonstrate the performance of the proposed method, that efficiently classify the binary/five classes of heart sounds. The results reveal that the proposed method has the overall accuracy values of 93.46% and 99.28% for the two datasets, which is better in comparison to other previously reported state-of-the-art techniques.

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Prediction of Heart Abnormality Using Heart Sound Signals

Gadde,

Kumar

2023

Lecture Notes in Electrical Engineering

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Classification of normal/abnormal PCG recordings using a time–frequency approach

Cited by 11 publications

References 32 publications

Vibration Signal Processing for Multirotor UAVs Fault Diagnosis: Filtering or Multiresolution Analysis?

Vibration Signal Processing for Multirotor UAVs Fault Diagnosis: Filtering or Multiresolution Analysis?

Automated Heart Valve Disorder Detection Based on PDF Modeling of Formant Variation Pattern in PCG Signal

Prediction of Heart Abnormality Using Heart Sound Signals

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