A Hybrid Particle Swarm Optimization and Neural Network with Fuzzy Membership Function Technique for Epileptic Seizure Classification

A hybrid particle swarm optimization (PSO) integrating neural network with fuzzy membership function (NEWFM) technique is proposed for epileptic seizure classification tasks based on brain electroencephalography (EEG) signals. By combining PSO and NEWFM, the proposed method obtains the optimal parameters from the EEG data training required to achieve the best accuracy in disease diagnosis. NEWFM, a model of neural networks, is expected to improve the accuracy by updating weights of fuzzy membership functions. The PSO, a swarm-inspired optimization algorithm, is used to obtain the optimal parameters from the NEWFM. A standard dataset comprising of 5 sets of epileptic seizure detection data, each consisting 100 single EEGs segments is employed to evaluate the proposed technique’s performance. Based on the experiments, the classification results show that the best accuracy of Z–S classification task is 99.5% with the optimal parameters of α = 0.1 and β=0.1. For the ZNF–S classification task, the best accuracy is 97.73% with the optimal parameters of α=0.1 or 0.2 and β =0.2. Similar results for the ZNFO–S classification task is 97.64% with the optimal parameters set at α =0.1 or 0.2 and β = 0.1.

Fuzzy Association Rule Mining Based Myocardial Ischemia Diagnosis on ECG Signal

A fuzzy association rule mining based method is proposed for myocardial ischemia diagnosis on ECG signals. The proposal provides interpretable and understandable information to doctors as an assistant reference, while rule mining on fuzzy itemsets guarantees that the feature segmentation before rule extraction is feasible and effective. A set of fuzzy association rules is mined through experiments on data from the European ST-T Database, and classification results of myocardial ischemia and normal heartbeats on the test dataset using the extracted rules obtained values of 83.4%, 80.7%, and 81.4% for sensitivity, specificity, and accuracy, respectively. The proposed method aims to become a helpful tool to accelerate the diagnosis of myocardial ischemia on ECG signal, and to be expanded to other heart disease diagnosis areas such as hypertensive heart disease and arrhythmia.

Segmented Wavelet Decomposition for Capnogram Feature Extraction in Asthma Classification

A feature extraction method from capnograms used for classifying asthma is proposed based on wavelet decomposition. Its computational cost is low and its performance is adequate for classifying asthma in real time. Experiments performed using 23 capnograms from an asthma camp in Cuba showed 97.39% best classification accuracy. The time required for a physiological multiparameter monitor to determine the suitable features of capnograms averaged 8 seconds. The proposal is to be used as part of a decision support system for asthma classification being developed by TITECH and TMDU research groups.

An Assessment Tool for Effective Monitoring of Autonomic Nervous System Activity in Healthy People

This paper proposes an application that analyzes and displays electrocardiograms (ECG; electrical activity of the heart over time) and plethysmograms (PTG; pulse waves produced by the heart pumping blood to the periphery) measured simultaneously. Recently in developed countries, chronic conditions typified by lifestyle-related diseases have become the leading cause of death. Simplified monitoring of the condition can be an effective approach to disease prevention and health promotion. We have focused on autonomic nervous system activity (ANSA) because it responds to stress as well as to changes in dietary patterns, and is correlated with hypertension, the source of some diseases, such as coronary disease. In this paper, we deal with both ECGs and PTGs as part of the biological data that reflects ANSA. The proposed application enables doctors to seamlessly negotiate analyzed waveforms and index charts of ECGs and PTGs in sync with each other. It also helps them comprehend the transition of ANSA. It offers a user interface (UI) that enables doctors to observe the two measures and the relationship between them for a quick assessment of ANSA; the sonification function of the ECG indices is implemented for providing the multi-modality of the UI. An experiment was conducted to confirm the feasibility of the analysis method of the application.