Classification of Atrial Fibrillation In ECG Signal Using Deep Learning

Atrial fibrillation is a type of heart rhythm disorder that most often occurs in the world and can cause death. Atrial fibrillation can be diagnosed by reading an Electrocardiograph (ECG) recording, however, an ECG reading takes a long time and requires specialists to analyze the type of signal pattern. The use of deep learning to classify Atrial Fibrillation in ECG signals was chosen because deep learning has 10% higher performance compared to machine learning methods. In this research, an application for classification of Atrial Fibrillation was developed using the 1-Dimentional Convolutional Neural Network (CNN 1D) method. There are 6 configurations of the 1D CNN model that were developed by varying the configuration on the learning rate and batch size. The best model obtained 100% accuracy, 100% precision, 100% recall, and 100% F1 Score

CLASSIFICATION OF ATRIAL FIBRILLATION IN ECG SIGNAL USING DEEP LEARNING

Atrial fibrillation is a type of heart rhythm disorder that most often occurs in the world and can cause death. Atrial fibrillation can be diagnosed by reading an Electrocardiograph (ECG) recording, however, an ECG reading takes a long time and requires specialists to analyze the type of signal pattern. The use of deep learning to classify Atrial Fibrillation in ECG signals was chosen because deep learning has 10% higher performance compared to machine learning methods. In this research, an application for classification of Atrial Fibrillation was developed using the 1-Dimentional Convolutional Neural Network (CNN 1D) method. There are 6 configurations of the 1D CNN model that were developed by varying the configuration on the learning rate and batch size. The best model obtained 100% accuracy, 100% precision, 100% recall, and 100% F1 Score

Automated ECG Waveform Annotation Based on Stacked Long Short-Term Memory

The classification of electrocardiogram (ECG) waveform segmentation techniques can be difficult due to physiological variation of heart rate and different characteristics of the different ECG waves in terms of shape, frequency, amplitude, and duration. The P-wave, PR-segment, QRS-complex, ST-segment, and T-wave are extracted as the feature for classification algorithm to diagnose specified cardiac disorders. This requires the implementation of algorithms that identify specific points within the ECG wave. Some previous computational algorithms for automatic classification of ECG segmentation are proposed to overcome limitations of manual inspection of the ECG. This study presents new insight into the ECG semantic segmentation problem is surmounted by a deep learning approach for automatic ECG wave-form. Long short-term memory (LSTM) is proposed for this task. This experimental study has been performed for six different waveforms of ECG signal that represents cardiac disorders obtained from the Physionet: QT database. Overall, LSTM performance achieved accuracy, sensitivity, specificity, precision, F1-score, is 93.36%, 86.85%, 95.78%, 81.79%, and 83.09%, respectively

Multiclass Segmentation of Pulmonary Diseases using Convolutional Neural Network

Pulmonary disease has affected tens of millions of people in the world. This disease has also become the cause of death of millions of its sufferers every year. In addition, lung disease has also become the cause of other respiratory complications, which also causes the death of the sufferer. The diagnosis of pulmonary diseases through medical imaging is a significant challenge in computer vision and medical image processing. The difficulty is due to the wide variety in infected areas' shape, dimension, and location. Another challenge is to differentiate one lung disease from the other. Discriminating pulmonary diseases is a notable concern in the diagnosis of pulmonary disease. We have adopted the deep learning convolutional neural network in this study to address these challenges. Seven models were constructed using the Mask Region-based Convolutional Neural Network (Mask-RCNN) architecture to detect and segment infected areas within the lung region from CT scan imagery. The evaluation results show that the best model obtained scores of 91.98%, 85.25%, and 93.75% for DSC, MIoU, and mAP, respectively. The segmentation results are then visualized

Securing Text File on Audio Files using Least Significant Bit (LSB) and Blowfish

Along with the development of technology, communication can be done in various ways, one of which is digital messages. But often the messages sent do not reach their destination and are obtained by irresponsible parties. This happens because of the lack of security in the file. For this reason, security is needed so that messages cannot be stolen or seen by other parties. There are various ways to secure messages, including Steganography and Cryptography techniques. This study uses a combination of the Least Significant Bit method and the Blowfish algorithm to secure secret messages in audio files. This research will measure encryption and decryption time, analysis of message file size changes after encryption and decryption, and PSNR value of audio files. The result of encryption using blowfish is a change in the size of the message file caused by the size of the message file is less than the block cipher size, so additional bytes are given so that the message size matches the block cipher size. The speed of the encryption and decryption process using the blowfish algorithm results in an average time for encryption of 547.98ms while the average time for decryption is 538.19ms. The longest time for the encryption process is 557.30ms and the fastest is 534.50ms, while the longest time for the decryption process is 548.74ms and the fastest is 531.46ms. Hiding messages in audio files using LSB produces PSNR values above 30dB

Unidirectional-bidirectional recurrent networks for cardiac disorders classification

The deep learning approach of supervised recurrent network classifiers model, i.e., recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) are used in this study. The unidirectional and bidirectional for each cardiac disorder (CDs) class is also compared. Comparing both phases is needed to figure out the optimum phase and the best model performance for ECG using the Physionet dataset to classify five classes of CDs with 15 leads ECG signals. The result shows that the bidirectional RNNs method produces better results than the unidirectional method. In contrast to RNNs, the unidirectional LSTM and GRU outperformed the bidirectional phase. The best recurrent network classifier performance is unidirectional GRU with average accuracy, sensitivity, specificity, precision, and F1-score of 98.50%, 95.54%, 98.42%, 89.93% 92.31%, respectively. Overall, deep learning is a promising improved method for ECG classification

A Deep Learning Approach to Integrate Medical Big Data for Improving Health Services in Indonesia

Medical Informatics to support health services in Indonesia is proposed in this paper. The focuses of paper to the analysis of Big Data for health care purposes with the aim of improving and developing clinical decision support systems (CDSS) or assessing medical data both for quality assurance and accessibility of health services. Electronic health records (EHR) are very rich in medical data sourced from patient. All the data can be aggregated to produce information, which includes medical history details such as, diagnostic tests, medicines and treatment plans, immunization records, allergies, radiological images, multivariate sensors device, laboratories, and test results. All the information will provide a valuable understanding of disease management system. In Indonesia country, with many rural areas with limited doctor it is an important case to investigate. Data mining about large-scale individuals and populations through EHRs can be combined with mobile networks and social media to inform about health and public policy. To support this research, many researchers have been applied the Deep Learning (DL) approach in data-mining problems related to health informatics. However, in practice, the use of DL is still questionable due to achieve optimal performance, relatively large data and resources are needed, given there are other learning algorithms that are relatively fast but produce close performance with fewer resources and parameterization, and have a better interpretability. In this paper, the advantage of Deep Learning to design medical informatics is described, due to such an approach is needed to make a good CDSS of health services

Pemanfaatan aplikasi daring untuk peningkatan pemasaran songket dan purun perajin Burai

Desa Burai merupakan salah satu desa yang terletak di Provinsi Sumatera Selatan. Masyarakat Desa Burai mayoritas memiliki mata pencaharian sebagai pengrajin songket dan pengrajin anyaman purun. Usaha rumah tangga ini mengalami kendala pada masalah pemasaran yang terbatas. Kegiatan pelatihan dan penyuluhan aplikasi daring seperti WhatsApp Business dan marketplace dapat menjadi alternatif dalam meningkatkan hasil kerajinan songket dan purun. Selama ini pemasaran yang dilakukan oleh pengrajin songket dan kerajinan anyaman purun masih dengan cara manual tanpa bantuan aplikasi daring. Tim memberdayaan penggunaan apliaksi daring yaitu media sosial WhatsApp Business dan marketplace Shopee bagi para perajin songket dan anyaman purun. Kegiatan pendampingan dilakukan dengan cara memberikan penyuluhan berupa paparan meteri dan pelatihan. Untuk mengukur keberhasilan kegiatan dilakukan pre-test sebelum kegiatan pendampingan dimulai da post-test setelah kegiatan dilaksanakan. Dari uji yang dilakukan menunjukkan adanya perubahan yang signifikan dari peserta pada saat sebelum diberikan pendampingan dan setelah dilakukan pendampingan. Dari hasil pre-test dan post-test menunjukan adanya peningkatan lebih dari 20% setelah dilakukannya kegiatan. Hal menyimpulkan bahawa para perajin telah mampu menggunakan aplikasi daring untuk memasarkan produk mereka secara lebih luas

Identification of Indonesian Authors Using Deep Neural Networks

Author Name Disambiguation (AND) is a problem that occurs when a set of publications contains ambiguous names of authors, i.e. the same author may appear with different names (synonyms) in other published papers, or author (authors) who may be different who may have the same name (homonym). In this final project, we will design a model with a Deep Neural Network (DNN) classifier. The dataset used in this final project uses primary data sourced from the Scopus website. This research focuses on integrating data from Indonesian authors. Parameters accuracy, sensitivity and precision are standard benchmarks to determine the performance of the method used to solve AND problems. The best DNN classification model achieves 99.9936% Accuracy, 93.1433% Sensitivity, 94.3733% Precision. Then for the highest performance measurement, the case of Non Synonym-Homonym (SH) has 99.9967% Accuracy, 96.7388% Sensitivity, and 97.5102% Precision

Forecasting Of Intensive Care Unit Patient Heart Rate Using Long Short-Term Memory

Cardiac arrest remains a critical concern in Intensive Care Units (ICUs), with alarmingly low survival rates. Early prediction of cardiac arrest is challenging due to the complexity of patient data and the temporal nature of ICU care. To address this challenge, we explore the use of Deep Learning (DL) models, specifically Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), for forecasting ICU patient heart rates. We utilize a dataset extracted from the MIMIC III database, which poses the typical challenges of irregular time series data and missing values. Our research encompasses a comprehensive methodology, including data preprocessing, model development, and performance evaluation. Data preprocessing involves regularizing and imputing missing values, as well as data normalization. The dataset is partitioned into training, testing, and validation sets to facilitate model training and evaluation. Fine-tuning of hyperparameters is conducted to optimize each DL architecture's performance. Our results reveal that the GRU architecture consistently outperforms LSTM and BiLSTM in predicting heart rates, achieving the lowest RMSE and MAE values. The findings underscore the potential of DL models, particularly GRU, in enhancing the early detection of cardiac events in ICU patients