Real-Time Event-Driven Classification Technique for Early Detection and Prevention of Myocardial Infarction on Wearable Systems
IEEE Transactions on Biomedical Circuits and Systems. 2018.
DOI : 10.1109/TBCAS.2018.2848477.
A considerable portion of government health-care spending is allocated to the continuous monitoring of patients suffering from cardiovascular diseases, particularly myocardial infarction (MI). Wearable devices present a cost-effective means of monitoring patients’ vital signs in ambulatory settings. A major challenge is to design such ultra-low energy devices for long-term patient monitoring. In this paper, we present a real-time event-driven classification technique based on the random forest classification scheme, which uses a confidence-related decision-making process. The main goal of this technique is to maintain a high classification accuracy while reducing the complexity of the classification algorithm. We validate our approach on a well-established and complete MI database (Physiobank, PTB Diagnostic ECG database ). Our experimental evaluation demonstrates that our real-time classification scheme outperforms the existing approaches in terms of energy consumption and battery lifetime by a factor of 2.60, with no classification quality loss.
e-Glass: A Wearable System for Real-Time Detection of Epileptic Seizures
2018. The International Symposium on Circuits and Systems (ISCAS) , Florence, Italy , May 27-30, 2018.
DOI : 10.1109/ISCAS.2018.8351728.
Today, epilepsy is one of the most common chronic diseases affecting more than 65 million people worldwide and is ranked number four after migraine, Alzheimer’s disease, and stroke. Despite the recent advances in anti-epileptic drugs, one-third of the epileptic patients continue to have seizures. More importantly, epilepsy-related causes of death account for 40% of mortality in high-risk patients. However, no reliable wearable device currently exists for real-time epileptic seizure detection. In this paper, we propose e-Glass, a wearable system based on four electroencephalogram (EEG) electrodes for the detection of epileptic seizures. Based on an early warning from e-Glass, it is possible to notify caregivers for rescue to avoid epilepsy-related death due to the underlying neurological disorders, sudden unexpected death in epilepsy, or accidents during seizures. We demonstrate the performance of our system using the Physionet.org CHB-MIT Scalp EEG database for epileptic children. Our experimental evaluation demonstrates that our system reaches a sensitivity of 93.80% and a specificity of 93.37%, allowing for 2.71 days of operation on a single battery charge.
Hierarchical Cardiac-Rhythm Classification Based on Electrocardiogram Morphology
2017. Computing in Cardiology (CinC) , Rennes, France , September 24-27, 2017.
Atrial Fibrillation (AF) is a type of cardiac arrhythmia that significantly increases the risk of stroke and heart failure. In general, in the case of patients affected by AF, their electrocardiogram (ECG) shows a typical pattern of irregular RR intervals and abnormal P waves. However, discriminating AF from a normal sinus rhythm or from other types of rhythms remains a challenging problem today. Methods: We analyze the database of PhysioNet/Computing in Cardiology Challenge 2017 to validate our heart rhythm classification technique. The database contains short-term ECG recordings, labelled as normal sinus rhythm, AF, other types of rhythm, and noise. We extract different morphology-based features of ECG signals, and we design a multiclass classifier based on error-correcting output codes, along with a random forest classifier for binary decision making. Results: We test the performance of our classifiers based on the F1 score of each class and the average F1 score of all the classes. The final F1 score obtained on the hidden test set of challenge is 80%. Conclusions: Our results show that our classifier is robust and that it is able to discriminate AF from normal sinus, other rhythms, and noise, based on the morphology of the ECG signal.
Real-time classification technique for early detection and prevention of myocardial infarction on wearable devices, 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)
2017. 13th IEEE Biomedical Circuits and Systems Conference , Turin, Italy , October 19-21, 2017. p. 1-4.
DOI : 10.1109/BIOCAS.2017.8325140.
Continuous monitoring of patients suffering from cardiovascular diseases and, in particular, myocardial infarction (MI) places a considerable burden on health-care systems and government budgets. The rise of wearable devices alleviates this burden, allowing for long-term patient monitoring in ambulatory settings. One of the major challenges in this area is to design ultra-low energy wearable devices for long-term monitoring of patients’ vital signs. In this work, we present a real-time event-driven classification technique, based on support vector machines (SVM) and statistical outlier detection. The main goal of this technique is to maintain a high classification accuracy while reducing the complexity of the classification algorithm. This technique leads to a reduction in energy consumption and thus battery lifetime extension. We validate our approach on a well-established and complete myocardial infarction (MI) database (Physiobank, PTB Diagnostic ECG database ). Our experimental evaluation demonstrates that our real-time classification scheme outperforms the existing approaches in terms of energy consumption and battery lifetime by a factor of 3, while maintaining the classification accuracy at a medically-acceptable level of 90%.
Touch-Based System for Beat-to-Beat Impedance Cardiogram Acquisition and Hemodynamic Parameters Estimation
2016. IEEE/ACM Design Automation and Test in Europe Conference (DATE'16) , Dresden, Germany , March 25-27, 2015. p. 150-155.
Among all cardiovascular diseases, congestive heart failure (CHF) has a very high rate of hospitalization and mortality. In order to prevent hospitalization, there is a strong need to identify patients at risk of a CHF event by estimating a set of relevant hemodynamic parameters that will allow physicians to detect its early onset. Today, one of the most popular non-invasive methods to obtain these parameters is through the acquisition of electrocardiogram (ECG) and impedance cardiogram (ICG) by using large hospital systems with electrodes placed on the chest and thorax region. In order to be useful in an ambulatory setting, it is important to obtain an ultra-low power wearable system for acquiring the ICG and ECG, and to detect CHF. In this paper, we present a touch-based ultra-low power device for real-time ICG and ECG signal acquisition, and hemodynamic parameters estimation. We also propose methods for noise cancellation and for calculating the hemodynamic parameters. In addition, a comparative evaluation of susceptibility to different measuring positions is presented. Our proposed design is highly correlated with traditional systems (> 80%), but able to work with very low power budgets, thus allowing long duration of over four days on a single battery charge.
Non-Linear EEG Features for Odor Pleasantness Recognition
2014. 6th International Workshop on Quality of Multimedia Experience , Singapore , September 18-20, 2014.
Since olfactory sense is gaining ground in multimedia applications, it is important to understand the way odor pleasantness is perceived. Although several studies have explored the way odor pleasantness perception influences the power spectral density of the electroencephalography (EEG) in various brain regions, there are still no studies that investigate the way odor pleasantness perception affects the non-linear temporal variations of EEG. In this study two non-linear metrics are used, namely permutation entropy, and dimension of minimal covers, to explore the possibility of recognizing odor pleasantness perception from the non-linear properties of EEG signals. The results reveal that it is possible to discriminate between pleasant and unpleasant odors from the EEG nonlinear properties, using a Linear Discriminant Analysis classifier with cross-validation.