Development of Respiratory Rate Estimation Technique Using Electrocardiogram and Photoplethysmogram for Continuous Health Monitoring

Nayan, Nazrul Anuar; Jaafar, Rosmina; Risman, Nur Sabrina

doi:10.11591/eei.v7i3.1244

Cited by 11 publications

(11 citation statements)

References 19 publications

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“…The signal is dismantled into P, Q, R, S and T waves globally as seen in Figure 1. The time interval between R of two successive ECG waves reveals the heart rate [6][7][8]. The highest amplitude is obtained from QRS complex which gives more information on the health condition of the heart [9].…”

Section: Research Methods 21 Characterization Of Ecgmentioning

confidence: 99%

On the classification of arrhythmia using supplementary features from Tetrolet transforms

Jayagopi

Pushpa

2019

IJECE

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<span>Heart diseases had been molded as potential threats to human lives, especially to elderly people in recent days due to the dynamically varying food habits among the people. However, these diseases could be easily caught by proper analysis of Electrocardiogram (ECG) signals acquired from individuals. This paper proposes a better method to detect and classify the arrhythmia using 15 features which include 4 R-R interval features, 3 statistical and 6 chaotic features estimated from ECG signals. Additionally, Entropy and Energy features had been gained after converting one dimensional ECG signals to two dimensional data and applied Tetrolet transforms on that. Total numbers of 15 features had been utilized to classify the heart beats from the benchmark MIT-Arrhythmia database using Support Vector Machines (SVM). The classification performance was analyzed under various kernel functions and different Tetrolet decomposition levels. It is found that Radial Basis Function (RBF) kernel could perform better than linear and polynomial kernels. This research attempt yielded an accuracy of 99.35 % against the existing works. Moreover, addition of two more features had introduced a negligible overhead of time. Hence, this method is better suitable to detect and classify the Arrhythmia in both online and offline.</span>

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Section: Research Methods 21 Characterization Of Ecgmentioning

confidence: 99%

On the classification of arrhythmia using supplementary features from Tetrolet transforms

Jayagopi

Pushpa

2019

IJECE

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“…The Analog circuit is represented in Figure 6. The analog signal is converted into digital with the help of Arduino-UNO connected to a PC to acquire the signal and send the signal to the PC to be processed by MATLAB [20,21]. Figure 7 shows the PPG waveform.…”

Section: Analog Sensing Circuitmentioning

confidence: 99%

Noninvasive blood glucose monitoring system based on near-infrared method

Al-dhaheri¹,

Mekkakia-Maaza²,

Mouhadjer³

et al. 2020

IJECE

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Diabetes is considered one of the life-threatening diseases in the world which need continuous monitoring to avoid the complication of diabetes. There is a need to develop a non-invasive monitoring system that avoids the risk of infection problems and pain caused by invasive monitoring techniques. This paper presents a method for developing a noninvasive technique to predict the blood glucose concentration (BCG) based on the Near-infrared (NIR) light sensor. A prototype is developed using a finger sensor based on LED of 940 nm wavelength to collect photoplethysmography (PPG) signal which is variable depending on the glucose concentration variance, a module circuit to preprocess PPG signals is realized, which includes an amplifier and analog filter circuits, an Arduino UNO is used to analog-to-digital conversion. A digital Butterworth filterer is used to remove PPG signal trends, then detect the PPG data peaks to determine the relationship between the PPG signal and (BCG) and use it as input parameters to build the calibration model based on linear regression. Experiments show that the Root Mean Squares Error (RMSE) of the prediction is between 8.264mg/dL and 13.166 mg/dL, the average of RMSE is about 10.44mg/dL with a correlation coefficient (R^2) of 0.839, it is observed that the prediction of glucose concentration is in the clinically acceptable region of the standard Clark Error Grid (CEG).

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“…BR was extracted using the respiratory sinus arrhythmia (RSA). RSA, which is also known as respiratory-induced frequency variations [41], is the correlation between the variations of the heart rate cycle and respiratory system. Heart rate increases when a person breathes in and decreases when a person breathes out.…”

Section: Data Extractionmentioning

confidence: 99%

Cardiovascular Disease Prediction from Electrocardiogram by using Machine Learning Method: A Snapshot from the Subjects of the Malaysian Cohort

Nayan¹,

Hamid²,

Suboh³

et al. 2020

Preprint

Self Cite

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Background: Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. Results: This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. Conclusions: In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.

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Development of Respiratory Rate Estimation Technique Using Electrocardiogram and Photoplethysmogram for Continuous Health Monitoring

Cited by 11 publications

References 19 publications

On the classification of arrhythmia using supplementary features from Tetrolet transforms

On the classification of arrhythmia using supplementary features from Tetrolet transforms

Noninvasive blood glucose monitoring system based on near-infrared method

Cardiovascular Disease Prediction from Electrocardiogram by using Machine Learning Method: A Snapshot from the Subjects of the Malaysian Cohort

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