Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability

Introduction: Predicting ventricular arrhythmia Torsade de Pointes (TdP) caused by drug-induced cardiotoxicity is essential in drug development. Several studies used single biomarkers such as qNet and Repolarization Abnormality (RA) in a single cardiac cell model to evaluate TdP risk. However, a single biomarker may not encompass the full range of factors contributing to TdP risk, leading to divergent TdP risk prediction outcomes, mainly when evaluated using unseen data. We addressed this issue by utilizing multi-in silico features from a population of human ventricular cell models that could capture a representation of the underlying mechanisms contributing to TdP risk to provide a more reliable assessment of drug-induced cardiotoxicity.Method: We generated a virtual population of human ventricular cell models using a modified O’Hara-Rudy model, allowing inter-individual variation. IC50 and Hill coefficients from 67 drugs were used as input to simulate drug effects on cardiac cells. Fourteen features (dVmdtrepol, dVmdtmax, Vmpeak, Vmresting, APDtri, APD90, APD50, Capeak, Cadiastole, Catri, CaD90, CaD50, qNet, qInward) could be generated from the simulation and used as input to several machine learning models, including k-nearest neighbor (KNN), Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN). Optimization of the machine learning model was performed using a grid search to select the best parameter of the proposed model. We applied five-fold cross-validation while training the model with 42 drugs and evaluated the model’s performance with test data from 25 drugs.Result: The proposed ANN model showed the highest performance in predicting the TdP risk of drugs by providing an accuracy of 0.923 (0.908–0.937), sensitivity of 0.926 (0.909–0.942), specificity of 0.921 (0.906–0.935), and AUC score of 0.964 (0.954–0.975).Discussion and conclusion: According to the performance results, combining the electrophysiological model including inter-individual variation and optimization of machine learning showed good generalization ability when evaluated using the unseen dataset and produced a reliable drug-induced TdP risk prediction system

An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning

Heart-sound auscultation is one of the most widely used approaches for detecting cardiovascular disorders. Diagnosing abnormalities of heart sound using a stethoscope depends on the physician’s skill and judgment. Several studies have shown promising results in automatically detecting cardiovascular disorders based on heart-sound signals. However, the accuracy performance needs to be enhanced as automated heart-sound classification aids in the early detection and prevention of the dangerous effects of cardiovascular problems. In this study, an optimal heart-sound classification method based on machine learning technologies for cardiovascular disease prediction is performed. It consists of three steps: pre-processing that sets the 5 s duration of the PhysioNet Challenge 2016 and 2022 datasets, feature extraction using Mel frequency cepstrum coefficients (MFCC), and classification using grid search for hyperparameter tuning of several classifier algorithms including k-nearest neighbor (K-NN), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). The five-fold cross-validation was used to evaluate the performance of the proposed method. The best model obtained classification accuracy of 95.78% and 76.31%, which was assessed using PhysioNet Challenge 2016 and 2022, respectively. The findings demonstrate that the suggested approach obtained excellent classification results using PhysioNet Challenge 2016 and showed promising results using PhysioNet Challenge 2022. Therefore, the proposed method has been potentially developed as an additional tool to facilitate the medical practitioner in diagnosing the abnormality of the heart sound

Identifikasi Sinyal Congestive Heart Failure dengan Metode Convolutional Neural Network 1D

ABSTRAKPenyakit jantung merupakan salah satu penyebab utama kematian di dunia. Salah satu penyakit jantung yang perlu diperhatikan adalah congestive heart failure (CHF). CHF adalah suatu kondisi di mana jantung tidak mampu memompa darah ke seluruh tubuh. Penyakit ini dapat didiagnosis dengan EKG. Oleh karena itu, pada penelitian ini dibuat sebuah sistem yang dapat mengidentifikasi penyakit CHF secara otomatis menggunakan metode convolutional neural network (CNN) dengan 4 hidden layer dan 16 output channel, fully connected layer, dan aktivasi Softmax. Data yang digunakan dalam penelitian ini diambil dari MITBIH dan BIDMC. Penlitian ini memberikan akurasi 100%, sehingga deteksi penyakit CHF otomatis membantu staf medis mendiagnosis pasien untuk menerima perawatan yang tepat.Kata kunci: Elektrokardiogram (EKG), Convolutional Neural Network (CNN), Normal Sinus Rhythm (NSR), Congestive Heart Failure (CHF)ABSTRACTHeart disease is one of the leading causes of death in the world. One of the heart diseases that need to be considered is congestive heart failure (CHF). CHF is a condition in which the heart is unable to pump blood throughout the body. ECG can diagnose this disease. Therefore, this study created a system that can automatically identify CHF disease using the convolutional neural network (CNN) method with four hidden layers and 16 output channels, a fully connected layer, and Softmax activation. The data used in this study were taken from MIT-BIH and BIDMC. In this study provides 100% accuracy. Automated CHF disease detection helps medical staff diagnose patients to receive appropriate treatment.Keywords: Electrocardiogram (ECG), Convolutional Neural Network (CNN), Normal Sinus Rhythm (NSR), Congestive Heart Failure (CHF)

Sistem Otentikasi Biometrik Berbasis Sinyal EKG Menggunakan Convolutional Neural Network 1 Dimensi

ABSTRAKBiometrik merupakan salah satu analisis karakteristik individu yang saat ini banyak digunakan, seperti sidik jari, pengenalan suara, dan pengenalan wajah. Metode biometrik tersebut masih memiliki kelemahan seperti mudah untuk dimanipulasi. Oleh karena itu, penelitian ini akan menggunakan sinyal Elektrokardiogram (EKG) sebagai salah satu metode biometrik. Sinyal EKG memiliki keunikan pada setiap individu sehingga sulit untuk dimanipulasi. Penelitian ini mengembangkan sistem otentikasi biometrik berbasis sinyal EKG. Data yang digunakan berasal dari ECG-ID database dengan jumlah 90 subjek. Sinyal EKG yang digunakan hanya menggunakan gelombang PQRST sebagai input model Convolutional Neural Network 1 Dimensi (CNN). Hasil akurasi yang diperoleh menunjukkan 92.2%. Dengan demikian, sistem yang dikembangkan memungkinkan digunakan sebagai otentikasi biometrik.Kata kunci: Biometrik, Sinyal EKG, Convolutional Neural NetworkABSTRACTBiometrics is analyses individual characteristics that are currently widely used, such as fingerprints, voice recognition, and face recognition. The biometric method still has weaknesses, such as being easy to manipulate. Therefore, this study will use an Electrocardiogram (ECG) signal as a biometric method. The ECG signal is unique to each individual, so it is not easy to manipulate. This study develops a biometric authentication system based on ECG signals. The data used comes from the ECG-ID database with a total of 90 subjects. The ECG signal used only PQRST waves as input for the 1-Dimensional Convolutional Neural Network (CNN) model. The accuracy results obtained show 92.2%. Thus, the developed system allows it to be used as biometric authentication.Keywords: Biometric, ECG Signal, Convolutional Neural Networ

Peningkatan Kompetensi Guru dan Tenaga Kependidikan Pendidikan Anak Usia Dini Kecamatan Gedebage Melalui Pelatihan Desain Grafis

ABSTRAKGuru dan Tenaga Kependidikan Pendidikan Anak Usia Dini (GTK PAUD) merupakan ujung tombak dan garda terdepan yang akan membimbing proses tumbuhnya insan Indonesia yang cerdas, dan berkarakter. Guru dan tenaga kependidikan semestinya merupakan seorang yang berkarakter tangguh, memiliki kompetensi mendasar serta mampu berdaya saing dan terbuka dalam menerima segala perubahan yang terjadi sangat cepat pada zaman revolusi 4.0 ini. Secara actual, kondisi yang terjadi dilapangan menjabarkan bahwa GTK PAUD, terutama yang berasal dari jalur pendidikan nonformal, memiliki kualifikasi dan kompetensi yang sangat beragam. Berangkat dari kondisi ini, dirancang sebuah kegiatan pendidikan dan pelatihan GTK PAUD yang bertujuan untuk mempersiapkan mereka menjadi pendidik yang lebih professional. Kegiatan dilakukan secara tatap muka dan ditambah dengan pemberian tugas mandiri. Pada bagian akhir kegiatan, peserta memberikan saran dan umpan balik terkait kebermanfaatn dan kesesuain materi pelatihan untuk meningkatkan kompetensi GTK PAUD di Kecamatan Gedebage. Dari 38 responden yang mengisi umpan balik, terlihat tingkat kesesuai materi yang diberikan terhadap kebutuhan mitra sangat baik, yaitu sebesar 90,79%. Sejalan dengan hal tersebut, peserta juga berpendapat bahwa materi yang diberikan selama pelatihan sangat bermaanfaat untuk mendukung proses peningkatan kompetensi para GTK PAUD, dengan tingkat kebermanfaatan sebesar 92,11%.ABSTRACTTeachers and Education Personnel for Early Childhood Education (GTK PAUD) are the frontline leaders who will steer the process of developing intellectual and character-driven Indonesians. Teachers and education personnel should be individuals with strong character, basic competences, and the ability to be competitive and open to all changes that occur at a rapid pace in this era of revolution 4.0. Actually, the actual conditions explain GTK PAUD emerging from non-formal education, qualifications, and competencies that are highly various. Based on this circumstance, a GTK PAUD education and training activity was created with the goal of preparing them to become more professional instructors. Face-to-face activities are carried out, and independent assignments are added. At the end of the program, participants completed suggestions and feedback on the usefulness and suitability of the training materials for improving GTK PAUD competency in Gedebage district. According to the feedback from 38 respondents, the level of compliance of the material offered to the needs of partners was extremely good, at 90.79%. In line with this, participants believed the training material was highly effective to support the process of growing GTK PAUD competency, with a usefulness rate of 92.11 percent