The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder.

(1) Background and Goal: Several studies have investigated the association of sleep, diurnal patterns, and circadian rhythms with the presence and with the risk states of mental illnesses such as schizophrenia and bipolar disorder. The goal of our study was to examine actigraphic measures to identify features that can be extracted from them so that a machine learning model can detect premorbid latent liabilities for schizotypy and bipolarity. (2) Methods: Our team developed a small wrist-worn measurement device that collects and identifies actigraphic data based on an accelerometer. The sensors were used by carefully selected healthy participants who were divided into three groups: Control Group (C), Cyclothymia Factor Group (CFG), and Positive Schizotypy Factor Group (PSF). From the data they collected, our team performed data cleaning operations and then used the extracted metrics to generate the feature combinations deemed most effective, along with three machine learning algorithms for categorization. (3) Results: By conducting the training, we were able to identify a set of mildly correlated traits and their order of importance based on the Shapley value that had the greatest impact on the detection of bipolarity and schizotypy according to the logistic regression, Light Gradient Boost, and Random Forest algorithms. (4) Conclusions: These results were successfully compared to the results of other researchers; we had a similar differentiation in features used by others, and successfully developed new ones that might be a good complement for further research. In the future, identifying these traits may help us identify people at risk from mental disorders early in a cost-effective, automated way.

Analysis of a Pulse Rate Variability Measurement Using a Smartphone Camera.

Background: Heart rate variability (HRV) provides information about the activity of the autonomic nervous system. Because of the small amount of data collected, the importance of HRV has not yet been proven in clinical practice. To collect population-level data, smartphone applications leveraging photoplethysmography (PPG) and some medical knowledge could provide the means for it. Objective: To assess the capabilities of our smartphone application, we compared PPG (pulse rate variability (PRV)) with ECG (HRV). To have a baseline, we also compared the differences among ECG channels. Method: We took fifty parallel measurements using iPhone 6 at a 240 Hz sampling frequency and Cardiax PC-ECG devices. The correspondence between the PRV and HRV indices was investigated using correlation, linear regression, and Bland-Altman analysis. Results: High PPG accuracy: the deviation of PPG-ECG is comparable to that of ECG channels. Mean deviation between PPG-ECG and two ECG channels: RR: 0.01 ms-0.06 ms, SDNN: 0.78 ms-0.46 ms, RMSSD: 1.79 ms-1.21 ms, and pNN50: 2.43%-1.63%. Conclusions: Our iPhone application yielded good results on PPG-based PRV indices compared to ECG-based HRV indices and to differences among ECG channels. We plan to extend our results on the PPG-ECG correspondence with a deeper analysis of the different ECG channels.

Usability of HL7 and SNOMED CT standards in Java Persistence API environment.

Due to the need for an efficient way of communication between the different stakeholders of healthcare (e.g. doctors, pharmacists, hospitals, patients etc.), the possibility of integrating different healthcare systems occurs. However, during the integration process several problems of heterogeneity might come up, which can turn integration into a difficult task. These problems motivated the development of healthcare information standards. The main goal of the HL7 family of standards is the standardization of communication between clinical systems and the unification of clinical document formats on the structural level. The SNOMED CT standard aims the unification of the healthcare terminology, thus the development of a standard on lexical level. The goal of this article is to introduce the usability of these two standards in Java Persistence API (JPA) environment, and to examine how standard-based system components can be efficiently generated. First, we shortly introduce the structure of the standards, their advantages and disadvantages. Then, we present an architecture design method, which can help to eliminate the possible structural drawbacks of the standards, and makes code generating tools applicable for the automatic production of certain system components.