MEDITEL.

Determining the cause of seizures is a significant medical problem, as misdiagnosis can result in increased morbidity and even mortality of patients. The reported research evaluates the efficacy of using an artificial neural network (ANN) for determining epileptic seizure occurrences for patients with lateralized bursts of theta (LBT) EEGs. Training and test cases are acquired from examining records of 1,500 consecutive adult seizure patients. The small resulting pool of 92 patients with LBT EEGs requires using a jack-knife procedure for developing the ANN categorization models. The ANNs are evaluated for accuracy, specificity, and sensitivity on classification of each patient into the correct two-group categorization: epileptic seizure or non-epileptic seizure. The original ANN model using eight variables produces a categorization accuracy of 62%. Following a modified factor analysis, an ANN model utilizing just four of the original variables achieves a categorization accuracy of 68%.

Diagnosis, Computer-Assisted↗

PubMed2001

TERVA: system for long-term monitoring of wellness at home.

Long-term monitoring of physiological and psychosocial variables in out-hospital conditions would be beneficial for investigating changes in wellness status of an individual or to understand interaction between physiological and behavioral processes. We aimed to design a personal wellness monitoring system (TERVA), which would allow monitoring of wellness-related variables at home for several weeks or even months. The designed TERVA system runs on a laptop computer and interfaces with different measurement devices through a serial interface. Measured variables include beat-to-beat heart rate, motor activity, blood pressure, weight, body temperature, respiration, ballistocardiography, movements, and sleep stages. In addition, self-assessments of daily well-being and activities are stored by keeping a behavioral diary. To test the system, one healthy man used the system for 10 weeks. The system was successfully applied in out-hospital conditions. The success rate of the measurements was 70-91%, depending on the variable under consideration. The pilot study indicated that the recorded data accurately reflected the health status of the subject. The TERVA system provides a method to record and investigate wellness-related data over several weeks, or even months, outside the hospital among subjects capable of using a personal computer. Several applications of the system are discussed.

Diagnosis, Computer-Assisted↗