Abstract:
:Real-time Obstructive Sleep Apnea (OSA) episode detection and monitoring are important for society in terms of an improvement in the health of the general population and of a reduction in mortality and healthcare costs. Currently, to diagnose OSA patients undergo PolySomnoGraphy (PSG), a complicated and invasive test to be performed in a specialized center involving many sensors and wires. Accordingly, each patient is required to stay in the same position throughout the duration of one night, thus restricting their movements. This paper proposes an easy, cheap, and portable approach for the monitoring of patients with OSA, which collects single-channel ElectroCardioGram (ECG) data only. It is easy to perform from the patient's point of view because only one wearable sensor is required, so the patient is not restricted to keeping the same position all night long, and the detection and monitoring can be carried out in any place through the use of a mobile device. Our approach is based on the automatic extraction, from a database containing information about the monitored patient, of explicit knowledge in the form of a set of IF…THEN rules containing typical parameters derived from Heart Rate Variability (HRV) analysis. The extraction is carried out off-line by means of a Differential Evolution algorithm. This set of rules can then be exploited in the real-time mobile monitoring system developed at our Laboratory: the ECG data is gathered by a wearable sensor and sent to a mobile device, where it is processed in real time. Subsequently, HRV-related parameters are computed from this data, and, if their values activate some of the rules describing the occurrence of OSA, an alarm is automatically produced. This approach has been tested on a well-known literature database of OSA patients. The numerical results show its effectiveness in terms of accuracy, sensitivity, and specificity, and the achieved sets of rules evidence the user-friendliness of the approach. Furthermore, the method is compared against other well known classifiers, and its discrimination ability is shown to be higher.
journal_name
J Biomed Informjournal_title
Journal of biomedical informaticsauthors
Sannino G,De Falco I,De Pietro Gdoi
10.1016/j.jbi.2014.02.015subject
Has Abstractpub_date
2014-06-01 00:00:00pages
84-100eissn
1532-0464issn
1532-0480pii
S1532-0464(14)00054-9journal_volume
49pub_type
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journal_title:Journal of biomedical informatics
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journal_title:Journal of biomedical informatics
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pub_type: 临床试验,杂志文章,多中心研究
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journal_title:Journal of biomedical informatics
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journal_title:Journal of biomedical informatics
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