Prediction of Acute Cardiovascular disease events

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Prediction of Sudden cardiac death/arrhythmia from ambulant ECG data

Early diagnosis or prediction of ventricular arrhythmia (VA) may allow clinicians enough time to intervene for stopping its escalation causing Sudden Cardiac Death (SCD) and thus is an active research area in the field of cardiovascular disease (CVD) research. Over the decades the main emphasis has been put on studying the Heart Rate Variability (HRV) as a possible marker for the early diagnosis of VA. Recently it was found that HRV increases two hours before the onset of arrhythmia. Also, the beat-to-beat oscillations of T-wave amplitudes increase before the onset of VA. Despite these findings it is difficult to derive a temporal relationship of these markers unequivocally to the onset of VA. We aim to explore a prediction algorithm for the onset of VA through statistical analysis of wearable ECG data. The candidate is expected to perform a detailed literature review, work on widely available public databases and develop a low-complexity algorithm that can be mapped on a resource constrained embedded platform. Developing a statistical index or a rule, may help stratifying short-term risk of arrhythmia along with other prevalent clinical markers.​

Type of project: Internship, Thesis, Combination of internship and thesis

Duration: 6 months

Required degree: Master of Engineering Technology, Master of Bioengineering, Master of Engineering Science

Required background: Biomedical engineering, Computer Science, Electrotechnics/Electrical Engineering

Supervising scientist(s): For further information or for application, please contact: Dwaipayan Biswas (Dwaipayan.Biswas@imec.be)

Imec allowance will be provided.

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