Orthogonal subspace projection based framework to extract heart cycles from SCG signal

Choudhary, Tilendra; Bhuyan, M. K.; Sharma, L. N.

doi:10.1016/j.bspc.2019.01.005

Cited by 17 publications

(13 citation statements)

References 16 publications

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“…The advantages of GCG over other modalities are as follows: Small in size, accurate and readily available sensors [ 32 ], Only one sensor is required to perform the registration [ 32 ], No need for applying multiple electrodes [ 9 , 120 ], The signal is not affected by gravity [ 32 ], Signal registration is insensitive to the location of sensor relative to the heart [ 32 ], The possibility of: distinguishing the systolic and diastolic phases for analyses of left ventricular performance [ 80 , 121 ], embedding the sensor into smart clothes [ 122 ], performing the signal acquisition using sensors embedded in smartphones [ 64 , 71 , 123 ], Providing the opportunity to acquire respiratory movements for further analysis [ 69 , 113 ], High correlations with seismocardiograms [ 8 ], which are described since 1957 [ 30 , 92 ], Better noise rejection performance than in seismocardiograms [ 8 , 105 ], Better performance in PEP estimation than in SCG [ 112 ]. …”

Section: Discussionmentioning

confidence: 99%

“…The possibility of: distinguishing the systolic and diastolic phases for analyses of left ventricular performance [ 80 , 121 ], embedding the sensor into smart clothes [ 122 ], performing the signal acquisition using sensors embedded in smartphones [ 64 , 71 , 123 ], …”

Section: Discussionmentioning

confidence: 99%

“…Because the ECG is considered the gold standard in analyzing cardiac activity [ 46 ], the advantages of gyrocardiography are underlined below as follows: small in size, inexpensive and accurate sensors [ 32 ] which can be embedded in smart clothes [ 122 ] and smartphones [ 64 , 71 , 123 ], no need for applying multiple electrodes which may irritate the patient’s skin [ 9 , 120 ], providing the information on the mechanical aspects of the cardiac cycle [ 8 , 11 ] and consequently the ability to distinguish the systolic and diastolic phases for analyses of left ventricular performance [ 80 , 121 ], providing the opportunity for registering respiratory movements [ 79 ], and the feasibility of self-application of the GCG using a smartphone [ 64 , 71 ].…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications

Sieciński

Kostka

Tkacz

2020

Sensors

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Gyrocardiography (GCG) is a non-invasive technique of analyzing cardiac vibrations by a MEMS (microelectromechanical system) gyroscope placed on a chest wall. Although its history is short in comparison with seismocardiography (SCG) and electrocardiography (ECG), GCG becomes a technique which may provide additional insight into the mechanical aspects of the cardiac cycle. In this review, we describe the summary of the history, definition, measurements, waveform description and applications of gyrocardiography. The review was conducted on about 55 works analyzed between November 2016 and September 2020. The aim of this literature review was to summarize the current state of knowledge in gyrocardiography, especially the definition, waveform description, the physiological and physical sources of the signal and its applications. Based on the analyzed works, we present the definition of GCG as a technique for registration and analysis of rotational component of local cardiac vibrations, waveform annotation, several applications of the gyrocardiography, including, heart rate estimation, heart rate variability analysis, hemodynamics analysis, and classification of various cardiac diseases.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications

Sieciński

Kostka

Tkacz

2020

Sensors

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“…The delineation can also improve the ability of wearable SCG sensor nodes in the development of pervasive wireless body area networks [1]. An extensive investigation has been done for annotation of AO phase in the SCG signal [5], [8]- [13], [15], [16], [21]. Most of the studies employed ECG as a reference signal for finding out cardiac information from a SCG signal [11], [13], [15].…”

Section: Introductionmentioning

confidence: 99%

“…An extensive investigation has been done for annotation of AO phase in the SCG signal [5], [8]- [13], [15], [16], [21]. Most of the studies employed ECG as a reference signal for finding out cardiac information from a SCG signal [11], [13], [15]. The determination of IM, AO and AC points are proposed in [9] using a high pass filter and a triple integration technique.…”

Section: Introductionmentioning

confidence: 99%

Delineation and Analysis of Seismocardiographic Systole and Diastole Profiles

Choudhary,

Bhuyan,

Sharma

2020

Preprint

Self Cite

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Precise estimation of fiducial points of a seismocardiogram (SCG) signal is a challenging problem for its clinical usage. Delineation techniques proposed in the existing literature do not estimate all the clinically significant points of a SCG signal, simultaneously. The aim of this research work is to propose a delineation framework to identify IM, AO, IC, AC, pAC and MO fiducial points with the help of a PPG signal. The proposed delineation method processes a wavelet based scalographic PPG and an envelope construction scheme is proposed to estimate the prominent peaks of the PPG signal. A set of amplitudehistogram based decision rules is developed for estimation of SCG diastole phases, namely AC, pAC and MO. Subsequently, the systolic phases, IM, AO and IC are detected by applying diastole masking on SCG and decision rules. Experimental results on real-time SCG signals acquired from our designed dataacquisition-circuitry and their analysis show effectiveness of the proposed scheme. Additionally, these estimated parameters are analyzed to show the discrimination between normal breathing and breathlessness conditions.

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A Review of Unsupervised Machine Learning Frameworks for Anomaly Detection in Industrial Applications

Usmani

Happonen

Watada

2022

Lecture Notes in Networks and Systems

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Unsupervised learning, also known as unsupervised machine learning, analyzes and clusters unlabeled data utlizing machine learning techniques. Without human input, these algorithms discover patterns or groupings in the data. In the domain of abuse and network intrusion detection, interesting objects are often short bursts of activity rather than rare objects. Anomaly detection is a difficult task that requires familiarity and a good understanding of the data and the pattern does not correspond to the common statistical definition of an outlier as an odd item. The traditional algorithms need data preparations while unsupervised algorithms can be prepared so that they can handle the data in war format. Anomaly detection, sometimes referred to as outlier analysis is a data mining procedure that detects events, data points, and observations that deviates from the expected behaviour of a dataset. The unsupervised machine learning approaches have shown potential in static data modeling applications such as computer vision, and their use in anomaly detection is gaining attention. A typical data might reveal critical flaws, such as a software defect, or prospective possibilities, such as a shift in consumer behavior. Currently, academic literature does not really cover the topic of unsupervised machine learning techniques for anomaly detection. This paper provides an overview of the current deep learning and unsupervised machine learning techniques for anomaly detection and discusses the fundamental challenges in anomaly detection.

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Orthogonal subspace projection based framework to extract heart cycles from SCG signal

Cited by 17 publications

References 16 publications

Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications

Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications

Delineation and Analysis of Seismocardiographic Systole and Diastole Profiles

A Review of Unsupervised Machine Learning Frameworks for Anomaly Detection in Industrial Applications

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