AIMC Topic: Machine Learning

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Lung Sound Classification Using Snapshot Ensemble of Convolutional Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
We propose a robust and efficient lung sound classification system using a snapshot ensemble of convolutional neural networks (CNNs). A robust CNN architecture is used to extract high-level features from log mel spectrograms. The CNN architecture is ...

Disentangled Adversarial Transfer Learning for Physiological Biosignals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Recent developments in wearable sensors demonstrate promising results for monitoring physiological status in effective and comfortable ways. One major challenge of physiological status assessment is the problem of transfer learning caused by the doma...

Arrhythmia Classification using Deep Learning and Machine Learning with Features Extracted from Waveform-based Signal Processing.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Arrhythmia is a serious cardiovascular disease, and early diagnosis of arrhythmia is critical. In this study, we present a waveform-based signal processing (WBSP) method to produce state-of-the-art performance in arrhythmia classification. When perfo...

Automatic Detection of Respiratory Effort Related Arousals With Deep Neural Networks From Polysomnographic Recordings.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Sleep disorders have become more common due to the modern lifestyle and stress. The most severe case of sleep disorders called apnea is characterized by a complete breaking block, leading to awakening and subsequent sleep disturbances. The automatic ...

A machine learning method for automatic detection and classification of patient-ventilator asynchrony.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Patients suffering from respiratory failure are often put on assisted mechanical ventilation. Patient-ventilator asynchrony (PVA) can occur during mechanical ventilation, which cause damage to the lungs and has been linked to increased mortality in t...

Machine Learning with Imbalanced EEG Datasets using Outlier-based Sampling.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Epilepsy is a neurological disorder which causes seizures in over 65 million people worldwide. Recently developed implantable therapeutic devices aim to prevent symptoms by applying acute electrical stimulation to the seizure-generating brain region ...

Deep transfer learning for improving single-EEG arousal detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics. We investigate two deep transfer learning strategies for overcoming the channel mismatch problem for cases where two d...

Generating sequential electronic health records using dual adversarial autoencoder.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Recent studies on electronic health records (EHRs) started to learn deep generative models and synthesize a huge amount of realistic records, in order to address significant privacy issues surrounding the EHR. However, most of them only fo...

Risk prediction of delirium in hospitalized patients using machine learning: An implementation and prospective evaluation study.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest-based algorithm to iden...

Emerging Clinical Technology: Application of Machine Learning to Chronic Pain Assessments Based on Emotional Body Maps.

Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics
Depression and anxiety co-occur with chronic pain, and all three are thought to be caused by dysregulation of shared brain systems related to emotional processing associated with body sensations. Understanding the connection between emotional states,...