ECG Abnormality Detection Algorithm

Ahmed, Soha; Hilal‐Alnaqbi, Ali; Hemairy, Mohamed Al; Ahmad, Mahmoud Al

doi:10.14569/ijacsa.2018.090827

Cited by 2 publications

(2 citation statements)

References 9 publications

(31 reference statements)

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“…The proposed approaches for the extraction of multiple vital signs simultaneously can be utilized in several applications. With further development and optimization it can be used in the detection of heart failure, lungs and cardiac abnormalities detection 44 . Currently there are huge interests in developing e-doctor based system that incorporates several defined medical conditions composed of several vital signs 45 .…”

Section: Discussionmentioning

confidence: 99%

Simultaneous piezoelectric noninvasive detection of multiple vital signs

Allataifeh

Ahmad

2020

Sci Rep

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the monitoring of vital signs plays a key role in the diagnosis of several diseases. piezoelectric sensors have been utilized to collect a corresponding representative signal from the chest surface. the subject typically needs to hold his or her breath to eliminate the respiration effect. This work further contributes to the extraction of the corresponding representative vital signs directly from the measured respiration signal. The contraction and expansion of the heart muscles, as well as the respiration activities, will induce a mechanical vibration across the chest wall. The induced vibration is then captured by the piezoelectric sensor placed at the chest surface, which produces an electrical output voltage signal conformally mapped with the respiration-cardiac activities. During breathing, the measured voltage signal is composed of the cardiac cycle activities modulated along with the respiratory cycle activity. A representative model that incorporates the cardiac and respiratory activities is developed and adopted. The piezoelectric and the convolution theories along with Fourier transformation are applied to extract the corresponding cardiac activity signal from the respiration signal. All the results were validated step by step by a conventional apparatus, with good agreement observed. Monitoring vital signs is essential for daily health and medical diagnosis 1. Vital signs, such as electrocardiogram (ECG) measurements, heart rate 2 , respiration rate 3 , systolic pressure (SP), and diastolic pressure (DP) 4 , along with pressure pulse play an important role in determining the state of health of a subject 5. Various methods and systems have recently been developed to monitor such vital signs 6-12. These vital signs are extracted from the acquired signals that enable more effective safeguarding of health by allowing for the early detection of any disease or abnormality 13-15. Researchers have recently focused on developing a remote non-contact sensing system having the ability to perform accurate long-term continuous monitoring of human vital signs. Measuring vital signs (such as respiration rates, blood pressure, and heart rates) continuously and remotely without touching the patients can be an invaluable tool for physicians, as it can be used to make life-and-death decisions rapidly and make better decisions when long-term patient data are available 16. The systems used to monitor vital signs may prevent diseases and enhance quality of life, thus reducing the costs of health care 17. Noninvasive vital sign monitoring, which includes measurement of pulse oximetry 18 , capnography, blood pressure (BP) measurement, and the standard five-lead electrocardiogram 19 , has been used for patients who are in the intensive care unit 20 and in the operating room. A stepped frequency continuous wave (SFCW) radar is also used to measure the heart rate and respiration by transmitting high average power pulses with long duration and narrow bandwidth, then a signal-processing algorithm based on the state space method is...

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Section: Discussionmentioning

confidence: 99%

Simultaneous piezoelectric noninvasive detection of multiple vital signs

Allataifeh

Ahmad

2020

Sci Rep

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“…Ahlstrom and Tompkins have used digital filters for real time ECG signal processing [6] to denoise the signal and detect the QRS complex. Hargittai [7] used Savitzky-Golay Least-Squares Polynomial filters to preserve the details of the signal, Francisco et al [8] process ECG signal using principal component analysis (PCA), Haque et al [9] use adaptive filtering algorithms, while Ahmed et al [10] use a method based on cross correlation theory. Gustavo et al [11] propose a comparison between several methods to remove baseline wander.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram signals classification using discrete wavelet transform and support vector machine classifier

Toulni

Nsiri

Taoufiq

2021

IJ-AI

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The electrocardiography allowed us to make a diagnosis of several cardiovascular diseases by representing the electrical activity of the heart over time; this representation is called the electrocardiogram (ECG) signal. In this study we have proposed a model based on the processing of the ECG signal by the wavelet decomposition using discrete wavelet transform (DWT). This decomposition firstly makes it possible to denoise the signal then to extract the statistical features from the approximation coefficients of the denoised signal and finally to classify the data obtained in a support vector machine (SVM) classifier with cross validation for more credibility. After having tested this model with different mother wavelets at different scales, the accuracies at the fourth scale are high and the best accuracy obtained is 87.50%.

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ECG Abnormality Detection Algorithm

Cited by 2 publications

References 9 publications

Simultaneous piezoelectric noninvasive detection of multiple vital signs

Simultaneous piezoelectric noninvasive detection of multiple vital signs

Electrocardiogram signals classification using discrete wavelet transform and support vector machine classifier

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