Comparative Analysis of Filters for Cancellation of Power-line-interference of ECG Signal

Bhoi, Akash Kumar; Sherpa, Karma Sonam; Khandelwal, Bidita

doi:10.7546/ijba.2019.23.3.000500

Cited by 7 publications

(9 citation statements)

References 6 publications

(8 reference statements)

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“…Classification accuracy of 96.39% is obtained with sensitivity and specificity of 96.77% 95.89%, respectively. Moreover, stride intervals obtained from VGRF signals of gait in neurodegenerative disease database has been employed with LDA, [105] Random Forest using TMA framework, [106] SVM, [107] HMM, [90] Decision tree [124] to discriminate PD from HD, ALS, and healthy subjects. Khoury et al [89] obtained 90% accuracy in differentiating PD from ALS, HD, and healthy groups with a K-NN classifier.…”

Section: Lstmmentioning

confidence: 99%

Role of Wearable Sensors with Machine Learning Approaches in Gait Analysis for Parkinson's Disease Assessment: A Review

Balakrishnan¹,

Medikonda²,

Namboothiri³

et al. 2022

Eng. Sci.

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Gait analysis, a way of assessing the manner of walking, is considered a significant criterion in diagnosing movement disorder. Various factors contribute to the alterations in gait patterns, of which neurodegenerative related disorders play a major role. Subjects affected by Parkinson's disease (PD) suffer from numerous gait-related disturbances, eventually worsening the Quality of Life. Artificial intelligence-based tools have shown great interest in computer-assisted diagnosis with the recent advancements in technology. This review article aims at portraying a novel collective approach of accenting every facet of PD gait by emphasizing the role of quantitative gait analysis and state-of-art technologies in the betterment of clinical diagnosis. The paper includes all the relevant research works (2014-2021) regarding PD assessments categorized as 1) Classification of PD and Healthy subjects, 2) PD severity prediction, and 3) Freezing of Gait detection by only considering gait modality as the mode of assessment.

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Section: Lstmmentioning

confidence: 99%

Role of Wearable Sensors with Machine Learning Approaches in Gait Analysis for Parkinson's Disease Assessment: A Review

Balakrishnan¹,

Medikonda²,

Namboothiri³

et al. 2022

Eng. Sci.

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“…A challenging task in PCG signals analysis is the localization of the heart sounds, especially S1 and S2. Because stethoscopes are sensitive to environmental noises and other sounds from the human body (e.g., respiratory sounds, lung sounds, rumbling of the stomach and intestine), denoising is strongly required to improve the accuracy of heart sounds localization [ 10 , 17 , 18 , 19 , 20 , 21 ]. Several techniques, such as short-time Fourier transform, fast Wavelet transform, tunable-Q Wavelet transform, and S transform, have been used to accomplish this task [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Accurate Localization of First and Second Heart Sounds via Template Matching in Forcecardiography Signals

Centracchio,

Parlato,

Esposito

et al. 2024

Sensors

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Cardiac auscultation is an essential part of physical examination and plays a key role in the early diagnosis of many cardiovascular diseases. The analysis of phonocardiography (PCG) recordings is generally based on the recognition of the main heart sounds, i.e., S1 and S2, which is not a trivial task. This study proposes a method for an accurate recognition and localization of heart sounds in Forcecardiography (FCG) recordings. FCG is a novel technique able to measure subsonic vibrations and sounds via small force sensors placed onto a subject’s thorax, allowing continuous cardio-respiratory monitoring. In this study, a template-matching technique based on normalized cross-correlation was used to automatically recognize heart sounds in FCG signals recorded from six healthy subjects at rest. Distinct templates were manually selected from each FCG recording and used to separately localize S1 and S2 sounds, as well as S1–S2 pairs. A simultaneously recorded electrocardiography (ECG) trace was used for performance evaluation. The results show that the template matching approach proved capable of separately classifying S1 and S2 sounds in more than 96% of all heartbeats. Linear regression, correlation, and Bland–Altman analyses showed that inter-beat intervals were estimated with high accuracy. Indeed, the estimation error was confined within 10 ms, with negligible impact on heart rate estimation. Heart rate variability (HRV) indices were also computed and turned out to be almost comparable with those obtained from ECG. The preliminary yet encouraging results of this study suggest that the template matching approach based on normalized cross-correlation allows very accurate heart sounds localization and inter-beat intervals estimation.

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“…Blood pressure is the most crucial cardiopulmonary measure, although it is difficult to monitor. Blood flow, which is correlated with blood pressure, is a second significant metric [9], [10]. Although ultrasound technology allows to check the blood flow in major arteries but practically it cannot be used frequently.…”

Section: Introductionmentioning

confidence: 99%

Development of Adaptive Spectrum Noise Cancellation Technique for Enhancing Heartbeat Rate Monitoring

Upadhyaya¹,

Jyothi²,

Haria³

2023

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Most of the Heart Rate Monitor (HRM) seems to be a portable monitoring device that can measure and displays real-time heartbeat rate in addition to storing heartbeat rate data for future studies. It is generally used to capture heart rate data during various types of physical exercise. Heart rhythm problems arise whenever the electrical impulses that coordinate the heartbeats aren't working properly. The ineffective signaling causes the heart to beat too fast (tachycardia), overly slowly (bradycardia), or sporadically. In present study, a focused approach on Adaptive Spectrum Noise Cancellation (ASNC) were used for enhancing heartbeat rate monitoring for humans. It finds that simply a photodetector and a source of light are applied to the surface of the skin to monitor the proportional changes in the blood flow. It is concluded that for accurate measurement of heart rate while moving or running, the proposed device ASNC utilizes the application of embedded accelerometer and gyroscope sensors to identify and eradicate the artifacts adaptively. In future, the proposed model may adopted for modification and commercialization of wearable heart-rate sensors based upon photoplethysmography (PPG), it would be better in terms of utility as well as adoptability.

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Comparative Analysis of Filters for Cancellation of Power-line-interference of ECG Signal

Cited by 7 publications

References 6 publications

Role of Wearable Sensors with Machine Learning Approaches in Gait Analysis for Parkinson's Disease Assessment: A Review

Role of Wearable Sensors with Machine Learning Approaches in Gait Analysis for Parkinson's Disease Assessment: A Review

Accurate Localization of First and Second Heart Sounds via Template Matching in Forcecardiography Signals

Development of Adaptive Spectrum Noise Cancellation Technique for Enhancing Heartbeat Rate Monitoring

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