CardioFit: Affordable Cardiac Healthcare Analytics for Clinical Utility Enhancement

Ukil, Arijit; Bandyopadhyay, Soma; Puri, Chetanya; Singh, Rajesh Kumar; Pal, Arpan; Mandana, Kayapanda

doi:10.1007/978-3-319-49655-9_47

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…The result is depicted in Table 3. In fact, we have shown previously in our contribution in [10] that HRV computation is significantly accurate while investigating on clean PPG. This result further strengthens our claim of pre-processing efficacy.…”

Section: Clinical Utility: Cad Detection Efficacymentioning

confidence: 68%

Cardiac Condition Monitoring through Photoplethysmogram Signal Denoising Using Wearables: Can We Detect Coronary Artery Disease with Higher Performance Efficacy?

Ukil¹,

Bandyoapdhyay²,

Puri³

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Section: Clinical Utility: Cad Detection Efficacymentioning

confidence: 68%

Cardiac Condition Monitoring through Photoplethysmogram Signal Denoising Using Wearables: Can We Detect Coronary Artery Disease with Higher Performance Efficacy?

Ukil¹,

Bandyoapdhyay²,

Puri³

et al. 2016

2016 Computing in Cardiology Conference (CinC)

Self Cite

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“…However, presence of ambient noise and motion artifacts poses a challenge in correctly identifying the condition of patient, even to a medical expert. Denoising [12,10] can increase the disease detection accuracy for signals like PPG, ABP. Nonstationary sound signals like PCG are highly prone to the background noises, even in clinical settings.…”

Section: Importance Of Noise Eliminationmentioning

confidence: 99%

Classification of Normal and Abnormal Heart Sound Recordings through Robust Feature Selection

Puri¹,

Ukil²,

Bandyopadhyay³

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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We aim to develop a reliable and robust algorithm that accurately analyses a single from a single precordial location to determine the presence of heart abnormality for the Physionet/ Computing-in-Cardiology 2016 IntroductionWe aim to develop a reliable algorithm that accurately analyses a single short PCG recording (10-60s) from a single precordial location to determine the presence of heart abnormality.We have considered features based on the statistical parameters derived from the analysis of the PCG recordings in the time and time-frequency domains. We segmented each PCG sequence into four states, namely, first heart sound (S1) and second heart sound (S2), Systole and Diastole using Hidden Markov Model based Springer's improved version of Schmidt's method [8]. Different features like standard deviation of S2 intervals, mean value of ratio between wavelet coefficient energy of Diastole period and RR in each heart beat are derived and overall 54 features are selected that represent a broad spectrum of energy, frequency, and temporal signature of typical PCG signal. Further, using minimum Redundancy Maximum Relevance (mRMR) algorithm, top 5 features are identified from the initial pool of 54 features. Then, non-linear radial basis function based Support Vector Machine (SVM) classifier is trained with the reduced set of optimal feature set, where balanced training set of 630 normal and 630 abnormal PCG datasets are considered. We also used ensemble based supervised learning with bagging and boosting techniques to handle the skewed class distributions. The trained SVM classifier and ensemble based classifier have been tested with the validation datasets as well as uploaded to test with hidden PCG datasets [1].It is also noted that detection of pathological conditions based on machine learning techniques is flawed by a) variety of datasets b) presence of outside noise in the datasets and poses a greater challenge to detection of abnormality. We also detect signal quality to handle this problem. State-of-the-artSeveral articles are available in the literature on the analysis of the PCG signal. The segmentation of the heart sound is the first step to analyze the signal and for the identification of systolic and diastolic regions allowing for the detection of pathological events. Various existing literature are available for the segmentation of the PCG signal. Authors in [7], have applied an ensemble empirical mode decomposition (EEMD) based method combined with kurtosis features to detect the location of first (s1) and second (s2) heart sound. In [8] authors have proposed a

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Automated Cardiac Health Screening Using Smartphone and Wearable Sensors Through Anomaly Analytics

Ukil

Bandyopadhyay

2018

Mobile Solutions and Their Usefulness in Everyday Life

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CardioFit: Affordable Cardiac Healthcare Analytics for Clinical Utility Enhancement

Cited by 4 publications

References 12 publications

Cardiac Condition Monitoring through Photoplethysmogram Signal Denoising Using Wearables: Can We Detect Coronary Artery Disease with Higher Performance Efficacy?

Cardiac Condition Monitoring through Photoplethysmogram Signal Denoising Using Wearables: Can We Detect Coronary Artery Disease with Higher Performance Efficacy?

Classification of Normal and Abnormal Heart Sound Recordings through Robust Feature Selection

Automated Cardiac Health Screening Using Smartphone and Wearable Sensors Through Anomaly Analytics

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