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A holographic doctors’ assistant on the example of a wireless heart rate monitor

Proniewska K., Dołęga-Dołęgowski D., Dudek D.

Bio-Algorithms and Med-Systems

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2018

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Vol. 14, no. 2

art. no. 20180007

EN

Microsoft has created HoloLens glasses, a high-tech device used for holographic purposes, which is unique and superior to other available solutions. We present a new idea of a holographic assistant to doctors, using as an example a wireless patient data monitor. A dedicated application will be created to be used by doctors, allowing hands-free access to patient cards/data, reviewing of new/old examination results, and even the ability to work on real-time data. Doctors will be able to use this in the examination room, at a patient’s bedside, or in an entirely different location. Currently, analysis of patient data is done mostly by the doctor; however, huge progress in computer hardware performance and artificial intelligence (AI) algorithms has allowed the development of new methods used to analyze and classify patient examination results. In the same way that doctors learn and practice how to treat patients during their studies, algorithms can learn to spot abnormalities, allowing current technology and advanced AI algorithms to be joined in one high-tech solution that should provide initial assessment of patients’ health and give treatment guidance, if necessary.

2

Sleep-related breathing biomarkers as a predictor of vital functions

Proniewska K., Pręgowska A., Malinowski K. P.

Bio-Algorithms and Med-Systems

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2017

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Vol. 13, no. 1

43--49

EN

Because an average human spends one third of his life asleep, it is apparent that the quality of sleep has an important impact on the overall quality of life. To properly understand the influence of sleep, it is important to know how to detect its disorders such as snoring, wheezing, or sleep apnea. The aim of this study is to investigate the predictive capability of a dual-modality analysis scheme for methods of sleep-related breathing disorders (SRBDs) using biosignals captured during sleep. Two logistic regressions constructed using backward stepwise regression to minimize the Akaike information criterion were extensively considered. To evaluate classification correctness, receiver operating characteristic (ROC) curves were used. The proposed classification methodology was validated with constructed Random Forests methodology. Breathing sounds and electrocardiograms of 15 study subjects with different degrees of SRBD were captured and analyzed. Our results show that the proposed classification model based on selected parameters for both logistic regressions determine the different types of acoustic events during sleep. The ROC curve indicates that selected parameters can distinguish normal versus abnormal events during sleep with high sensitivity and specificity. The percentage of prediction for defined SRBDs is very high. The initial assumption was that the quality of result is growing with the number of parameters included in the model. The best recognition reached is more than 89% of good predictions. Thus, sleep monitoring of breath leads to the diagnosis of vital function disorders. The proposed methodology helps find a way of snoring rehabilitation, makes decisions concerning future treatment, and has an influence on the sleep quality.

3

Data mining with Random Forests as a methodology for biomedical signal classification

Proniewska K.

Bio-Algorithms and Med-Systems

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2016

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Vol. 12, no. 2

89--92

EN

As the contribution of specific parameters is not known and significant intersubject variability is expected, a decision system allowing adaptation for subject and environment conditions has to be designed to evaluate biomedical signal classification. A decision support system has to be trained in its desirable functionality prior to being used for patient monitoring evaluation. This paper describes a decision system based on data mining with Random Forests, allowing the adaptation for subject and environment conditions. This methodology may lead to specific system scoring by an artificial intelligence-supported patient monitoring evaluation system, which may help find a way of making decisions concerning future treatment and have influence on the quality of patients’ life.