A portable respiratory rate estimation system with a passive single-lead electrocardiogram acquisition module

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

doi:10.3233/thc-161145

Cited by 11 publications

(11 citation statements)

References 13 publications

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“…Combining Qigong breathing characteristics with unique pathologies can provide AI-guided medical interventions using treatment databases for those pathologies. e daily vitality score index (VSI) [33][34][35] is collected using AI monitoring to summarize the specific breathing characteristics of Qigong to guide patients to stay healthy. Qigong assessment of respiratory health combined with associated therapies and biomarkers like defining VSI can be useful in establishing tracking patterns for long-term health care.…”

Section: Evidence-based Complementary and Alternative Medicinementioning

confidence: 99%

Overview of Artificial Intelligence Applications in Chinese Medicine Therapy

Feng

Zhou

et al. 2021

Evidence-Based Complementary and Alternative Medicine

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To evaluate the importance of AI technologies in modernizing traditional Chinese medicine (TCM) therapy, this article presents the systematic review of the relevant literature and explains the beneficial effects of AI technology on the TCM treatment outcomes from the experience of famous and veteran Chinese medicines, including acupuncture, Tui Na massage, and Qigong practitioners. This study also focuses on the urgent necessity to apply AI technologies to develop therapeutic models on the theme “treating the disease before it happens.” Furthermore, the study also discusses the major bottlenecks and future prospects for the development of intelligent TCM treatment strategies.

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Section: Evidence-based Complementary and Alternative Medicinementioning

confidence: 99%

Overview of Artificial Intelligence Applications in Chinese Medicine Therapy

Feng

Zhou

et al. 2021

Evidence-Based Complementary and Alternative Medicine

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“…The wave formed by RSA was resampled at a frequency of 4 Hz through spline interpolation to perform fast Fourier transform (FFT). After the FFT process, the RSA waveforms were filtered using an FIR band pass filter with cutoff frequencies of 0.1 and 0.6 Hz, which were equivalent to the respiratory rates of 6-36 breaths per minute, to eliminate non-respiratory frequencies [42]. The respiratory signals were identified in a sinusoidal form.…”

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

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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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“…Shen et al [28] applied 1Hz to 50Hz IIR filter to remove baseline wander in their hardware while Raka et al [22] chose different bandwidth of 0.5Hz to 45Hz for the filter. Houshyarifar et al [9] and Ebrahimzahed et al [12] used two stages moving average filter while Devi et al [6] and Nayan et al [29] have used Fast Fourier Transform to identify and remove the noise. Meanwhile, Acharya et al [10] have used wavelet technique for denoising processes.…”

Section: Signal Pre-processingmentioning

confidence: 99%

ECG-based Detection and Prediction Models of Sudden Cardiac Death: Current Performances and New Perspectives on Signal Processing Techniques

et al. 2019

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Heart disease remains the main leading cause of death globally and around 50% of the patients died due to sudden cardiac death (SCD). Early detection and prediction of SCD have become an important topic of research and it is crucial for cardiac patient’s survival. Electrocardiography (ECG) has always been the first screening method for patient with cardiac complaints and it is proven as an important predictor of SCD. ECG parameters such as RR interval, QT duration, QRS complex curve, J-point elevation and T-wave alternan are found effective in differentiating normal and SCD subjects. The objectives of this paper are to give an overview of SCD and to analyze multiple important ECG-based SCD detection and prediction models in terms of processing techniques and performance wise. Detail discussions are made in four major stages of the models developed including ECG data, signal pre-processing and processing techniques as well as classification methods. Heart rate variability (HRV) is found as an important SCD predictor as it is widely used in detecting or predicting SCD. Studies showed the possibility of SCD to be detected as early as one hour prior to the event using linear and non-linear features of HRV. Currently, up to 3 hours of analysis has been carried out. However, the best prediction models are only able to detect SCD at 6 minutes before the event with acceptable accuracy of 92.77%. A few arguments and recommendation in terms of data preparation, processing and classification techniques, as well as utilizing photoplethysmography with ECG are pointed out in this paper so that future analysis can be done with better accuracy of SCD detection accuracy.

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A portable respiratory rate estimation system with a passive single-lead electrocardiogram acquisition module

Cited by 11 publications

References 13 publications

Overview of Artificial Intelligence Applications in Chinese Medicine Therapy

Overview of Artificial Intelligence Applications in Chinese Medicine Therapy

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

ECG-based Detection and Prediction Models of Sudden Cardiac Death: Current Performances and New Perspectives on Signal Processing Techniques

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