In this paper, we proposed the implementation of a simple and lightweight Atrial Fibrillation detection based on an improved Variable Step Dynamic Threshold Local Binary Pattern algorithm. Using feature selection based on correlation and statistical significance, we can reduce the input feature size to just 44 features without significantly degrading classification accuracies. Tested on 15-second signal segments from the MIT-BIH Atrial Fibrillation Database and combined with a support vector machine classifier, the proposed method can achieve 99.14% sensitivity, 99.12% specificity, and 99.13% accuracy. When the input signal length is 60 seconds, the sensitivity, specificity, and accuracy are 99.49%, 99.46%, and 99.47%, respectively. The reduced input feature size results in a machine learning model size as small as 132.86kB when the input signal length is 60 seconds. When implemented on an Arm Cortex M4-based STM32F413ZHT3 microprocessor with 100MHz clock frequency, the proposed method can achieve similar performance as a PC-based system with an average current consumption of just 27mA. The embedded C program can fit in as small as 114.46kB of flash memory and complete one SVM inference as fast as 11.28ms. The results presented in this paper show that it is possible to do highly accurate machine learning classification that can detect Atrial Fibrillation from ECG signals on a low-power, constrained resource microcontroller. Our results will make developing a high quality, low cost, and low power wearable smart medical electronic device for detecting atrial fibrillation from ECG signal much easier.

Background: Obstructive Sleep Apnea (OSA) is a respiratory disorder due to obstructive upper airway (mainly in the oropharynx) periodically during sleep. The common examination used to diagnose sleep disorders is Polysomnography (PSG). Diagnose with PSG feels uncomfortable for the patient because the patient’s body is fitted with many sensors. Objective: This study aims to propose an OSA detection using the Fast Fourier Transform (FFT) statistics of electrocardiographic RR Interval (R interval from one peak to the peak of the pulse of the next pulse R) and machine learning algorithms.Material and Methods: In this case-control study, data were taken from the Massachusetts Institute of Technology at Beth Israel Hospital (MIT-BIH) based on the Apnea ECG database (RR Interval). The machine learning algorithms were Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM). Results: The OSA detection technique was designed and tested, and five features of the FFT were examined, namely mean (f1), Shannon entropy (f2), standard deviation (f3), median (f4), and geometric mean (f5). The OSA detection found the highest performance using ANN. Among the ANN types tested, the ANN with gradient descent backpropagation resulted in the best performance with accuracy, sensitivity, and specificity of 84.64%, 94.21%, and 64.03%, respectively. The lowest performance was found when LDA was applied. Conclusion: ANN with gradient-descent backpropagation performed higher than LDA, SVM, and KNN for OSA detection.

Atrial fibrillation is a serious heart problem originated from the upper chamber of the heart. The common indication of atrial fibrillation is irregularity of R peak-to-R-peak time interval, which is shortly called RR interval. The irregularity could be represented using variance or spread of RR interval. This article presents a system to detect atrial fibrillation using variances. Using clinical data of patients with atrial fibrillation attack, it is shown that the variance of electrocardiographic RR interval are higher during atrial fibrillation, compared to the normal one. Utilizing a simple detection technique and variances of RR intervals, we find a good performance of atrial fibrillation detection.

QT-interval prolongation is an important parameter for heart arrhythmia diagnosis. It is the time interval from QRS-onset to the T-end of electrocardiogram (ECG). Manual measurement of QT-interval, especially for 12-leads ECG, is time-consuming. Hence, an automatic QT-interval measurement is necessary. A new method for automatic QT-interval measurement is presented in this paper, which mainly consists of three parts, including QRS-complex detection, determination of QRS-onset, and T-end determination. The QRS-complex detection is based on the modified Pan-Tompkins algorithm. The T-end is defined based on Region of Interest (ROI) maximum limit. We compare and test our proposed QT-interval measurement method with reference measurement in term of correlation coefficient and range of 95% LoA. The correlation coefficient and the range of 95% LoA are 0.575 and 0.290, respectively. The proposed method is successfully implemented in ECG monitoring system using smartphone with high performance. The accuracy, positive predictive, and sensitivity of the QRS-complex detection in the system are 99.70%, 99.78%, and 99.92%, respectively. The range of 95% LoA for the comparison between manual and the system’s QT-interval measurement is 0.216. The results show that the proposed method is dependable on the measure of the QT-interval and outperforms the other methods in term of correlation coefficient and range of 95% LoA.

This study was conducted to make Multi Electrode Resistivity Meter with simple and low-cost circuit design. The instrument that has been made use switching mode boost converter for increasing the voltage with Arduino Nano as a source of PWM signal connected to the boost converter circuit, and cascode current mirror circuit is used for constant current source. The sensor on this resistivity meter using the voltage divider circuit for voltage sensor, and non-inverting OPAMP circuit for current sensor. Multi electrode circuit using relay module connected with IC74HC595 and controlled by Arduino MEGA 2560. This instrument is integrated with GUI that serves as controller and data display. There are several measurements for this instrument that is maximum resistance on each constant current generated from the circuit, repeatability and reproducibility measurement, sensor calibration measurement, and ERT measurement with three configuration i.e wenner configuration, wenner-schlumberger configuration, and dipole-dipole configuration on soil samples.

Penelitian untuk deteksi aritmia pada elektrokardiogram dengan metode Jaringan Syaraf Tiruan (JST) Multilayer Perceptron-Backpropagation (MLP-BP) kelas jamak menggunakan fitur interval RR, interval QRS, serta gradien gelombang R telah berhasil dilaksanakan. Tipe aritmia yang dideteksi dalam penelitian adalah Premature Ventricular Contraction (PVC), Premature Atrial Contraction (PAC), dan Left Bundle Branch Block (LBBB). Pada penelitian ini dilakukan variasi jumlah fitur sebagai masukan JST, yaitu tiga macam fitur dan dua macam fitur. Hasil penelitian terbaik ditunjukkan pada variasi tiga macam fitur (interval RR, interval QRS, gradien gelombang R ) dengan kinerja berupa sensitivitas, spesifisitas, serta akurasi cukup baik yaitu 94,63%, 93,94%, serta 94,49%.