A Cardiac Early Warning System with Multi Channel SCG and ECG Monitoring for Mobile Health

Sahoo, Prasan Kumar; Thakkar, Hiren Kumar; Lee, Ming-Yih

doi:10.3390/s17040711

Cited by 69 publications

(38 citation statements)

References 45 publications

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“…Eighteen articles published between the years 2016-2018 were included in the review [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] from conference or journal. Research originated from eleven different countries including Germany [9,10], Romania [11,12], India [13][14][15][16][17][18], Bangladesh [19], Iraq [20], Italy [21], Taiwan [22], Netherlands [23], Ecuador [24], Indonesia [25] and United Kingdom [26].…”

Section: Search Resultsmentioning

confidence: 99%

“…Becomes one of the fastest developing medical research in recent years, the wearable sensor can be the alternative to perceive human condition [22]. Most of them build that system to monitor health, nevertheless the other benefit of this to promote the sports activity and combat physical inactivity thus raising awareness to maintain health [11].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Wearable Sensor and Internet of Things Technology for Better Medical Science: A Review

Putri¹,

Ali²,

Saad³

et al. 2018

IJET

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E-health becomes one of the internet's products for healthcare. The problems of health service such as far hospital and expensive examination fees become the emergence of this technology. Consequently, people reluctant to check their health to hospital. E-health provides information on disease prevention, detecting early symptoms, and monitoring the patient's condition based on medical parameters from a far distance. Internet of things became the main concept in this system, which combines wearable sensors, communication systems, and mobile user interfaces. Reliable and valid system, easily carried, help the doctor to monitor patients from far distance expectantly to overcome the problems. The aims of this paper review are describing how an internet of things technology and wearable sensor help medical science and find the best way to create a health monitoring system.

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Section: Search Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Wearable Sensor and Internet of Things Technology for Better Medical Science: A Review

Putri¹,

Ali²,

Saad³

et al. 2018

IJET

View full text Add to dashboard Cite

show abstract

“…Today, smartphones and smartwatches are common and can be used as part of telemedicine for real-time patient monitoring at a relatively low cost. Smartphones used SCG for continuous monitoring of heart rate variability [114], and cardiac activity of patients suffering from heart disease [115]. In a recent study, the feasibility and accuracy of measuring heart rate using a smartphone accelerometer was assessed in different postural positions [46] and suggested utility of SCG for heart rate estimation.…”

Section: Portable Detection Of Scgmentioning

confidence: 99%

Recent Advances in Seismocardiography

et al. 2019

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Cardiovascular disease is a major cause of death worldwide. New diagnostic tools are needed to provide early detection and intervention to reduce mortality and increase both the duration and quality of life for patients with heart disease. Seismocardiography (SCG) is a technique for noninvasive evaluation of cardiac activity. However, the complexity of SCG signals introduced challenges in SCG studies. Renewed interest in investigating the utility of SCG accelerated in recent years and benefited from new advances in low-cost lightweight sensors, and signal processing and machine learning methods. Recent studies demonstrated the potential clinical utility of SCG signals for the detection and monitoring of certain cardiovascular conditions. While some studies focused on investigating the genesis of SCG signals and their clinical applications, others focused on developing proper signal processing algorithms for noise reduction, and SCG signal feature extraction and classification. This paper reviews the recent advances in the field of SCG.

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“…Traditionally, detection of MI based on the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective [4]. In addition, it requires expertise in the analysis of long-term ECG or VCG signals [5]. In those application scenarios absence of experts, for example, in emergency situations, computer-aided automatic detection of MI becomes significant.…”

Section: Introductionmentioning

confidence: 99%

Myocardial infarction detection using artificial intelligence and nonlinear features based on synthesis of the standard 12-lead and Frank XYZ leads

Zeng

Lin

Yuan

2020

Preprint

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Nowadays cardiovascular diseases ( CVD ) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention of CVD . Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgment and several electrocardiographic (ECG) or vectorcardiographic ( VCG ) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection techniques including signal processing and artificial intelligence tools have been developed. In this study we propose a novel technique for automatic detection of MI based on disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are reduced to 3-dimensional VCG signals, which are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector. This vector is decomposed into a series of proper rotation components ( PRCs ) by using the intrinsic time-scale decomposition ( ITD ) method. Second, four levels discrete wavelet transform ( DWT ) is employed to decompose the predominant PRCs into different frequency bands, in which third-order Daubechies ( db3 ) wavelet function is selected as reference variable for analysis. Third, phase space of the reference variable is reconstructed based on db3 , in which the properties associated with the nonlinear cardiac system dynamics are preserved. Three-dimensional ( 3D ) phase space reconstruction ( PSR ) together with Euclidean distance (ED) has been utilized to derive features. Fourth, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. Finally, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the 10-fold cross-validation style, the achieved average classification accuracy is reported to be 98.20 % . The result verifies the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.

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A Cardiac Early Warning System with Multi Channel SCG and ECG Monitoring for Mobile Health

Cited by 69 publications

References 45 publications

Wearable Sensor and Internet of Things Technology for Better Medical Science: A Review

Wearable Sensor and Internet of Things Technology for Better Medical Science: A Review

Recent Advances in Seismocardiography

Myocardial infarction detection using artificial intelligence and nonlinear features based on synthesis of the standard 12-lead and Frank XYZ leads

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