MATERIALS AND METHODS: In this case-control study, we examined 294 patients over the age of 18 who were using synthetic cathinones and opioids. All patients underwent electrocardiography and transthoracic echocardiography.
RESULTS: Our study involved 183 patients using synthetic cathinones and 111 patients reporting opioid use. The average age of the patients was 32.4 ± 8.5 years. In patients using synthetic cathinones, electrocardiography showed a lengthening in the average duration of the ventricular QRS complex (70.5 ± 13.3 ms vs. 69.6 ± 11.7 ms), T wave (154.1 ± 27.5 ms vs. 140.4 ± 24.1 ms), and QT interval (338.2 ± 28.5 ms vs. 334.8 ± 33.5 ms), as well as a shortening of the P wave (79.1 ± 12.2 ms vs. 82.6 ± 14.4 ms) and PQ interval (146.4 ± 19.6 ms vs. 148.3 ± 20.1 ms). Echocardiography confirmed left ventricular hypertrophy in 10.9% of the synthetic cathinones group and 17.1% of the opioid group. Transmitral left ventricular diastolic dysfunction was diagnosed in 23.5% of patients in both groups. Additionally, 31.1% of patients using synthetic cathinones and 44.1% of those using opioids had a reduced ejection fraction on echocardiography.
CONCLUSION: In patients using synthetic cathinones the QT interval was longer compared to those using opioids. The ejection fraction was lower in the opioid group. Electrocardiographic and echocardiographic screening should be conducted for all patients with post-acute withdrawal syndrome to prevent life-threatening arrhythmias and heart failure.
METHODS: The dataset used in this study consist of ECG data collected from 45 ADHD, 62 ADHD+CD, and 16 CD patients at the Child Guidance Clinic in Singapore. The ECG data were segmented into 2 s epochs and directly used to train our 1-dimensional (1D) convolutional neural network (CNN) model.
RESULTS: The proposed model yielded 96.04% classification accuracy, 96.26% precision, 95.99% sensitivity, and 96.11% F1-score. The Gradient-weighted class activation mapping (Grad-CAM) function was also used to highlight the important ECG characteristics at specific time points that most impact the classification score.
CONCLUSION: In addition to achieving model performance results with our suggested DL method, Grad-CAM's implementation also offers vital temporal data that clinicians and other mental healthcare professionals can use to make wise medical judgments. We hope that by conducting this pilot study, we will be able to encourage larger-scale research with a larger biosignal dataset. Hence allowing biosignal-based computer-aided diagnostic (CAD) tools to be implemented in healthcare and ambulatory settings, as ECG can be easily obtained via wearable devices such as smartwatches.
METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.
RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.
CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.