Biomedical subjects

Helge Myklebust

Publications and source records attributed to Helge Myklebust.

3 recordsLinked to original sources

PubMed2002

Retention of basic life support skills 6 months after training with an automated voice advisory manikin system without instructor involvement.

AIM: To evaluate the retention of skills 6 months after training in ventilation and chest compressions (CPR) on a manikin with computer based on-line voice advisory feedback and the possible effects of initial overtraining. METHODS: Thirty five volunteers had 20 min provisional CPR training on a manikin with computer based voice advisory feedback but without an instructor. The appropriate feedback was taken from a pre-recorded list depending on performance measured by the manikin--computer system versus set limits for ventilation and compression variables. One group in addition was randomised to receive 10 similar 3 min training sessions during 1 week in the following month (overtrained group). All ventilation and compression variables were measured without feedback before and after the initial training session, with feedback immediately thereafter, and both without and with feedback 6 months after the initial training session. RESULTS: The initial training improved all variables. Compressions with correct depth increased from a mean of 33 to 77%, and correct inflations from a mean of 9 to 58%. After 6 months, the results for the controls were not significantly different from pre-training, except for a higher of correct inflations (18%), while the overtrained group had better retention of skills including the correct compression depth (mean 61%) and inflations (mean 42%). When verbal feedback was added both the compressions and ventilations immediately improved both when tested immediately and 6 months after the initial training session. CONCLUSIONS: The computer-based voice advisory manikin (VAM) feedback system can improve immediate performance of basic life support (BLS) skills, with better long-term retention with overtraining.

Adult↗

PubMed2002

Compression depth estimation for CPR quality assessment using DSP on accelerometer signals.

Chest compression is a vital part of cardiopulmonary resuscitation (CPR). This paper demonstrates how the compression depth can be estimated using the principles of inertia navigation. The proposed method uses accelerometer sensors, one placed on the patient's chest, the other beside the patient. The acceleration-to-position conversion is performed using discrete-time digital signal processing (DSP). Instability problems due to integration are combated using a set of boundary conditions. The proposed algorithm is tested on a mannequin in harsh environments, where the patient is exposed to external forces as in a boat or car, as well as improper sensor/patient alignment. The overall performance is an estimation depth error of 4.3 mm in these environments, which is reduced to 1.6 mm in a regular, flat-floor controlled environment.

Algorithms↗

PubMed2002

Removal of cardiopulmonary resuscitation artifacts from human ECG using an efficient matching pursuit-like algorithm.

We present a computationally efficient and numerically robust solution to the problem of removing artifacts due to precordial compressions and ventilations from the human electrocardiogram (ECG) in an emergency medicine setting. Incorporated into automated external defibrillators, this would allow for simultaneous ECG signal analysis and administration of precordial compressions and ventilations, resulting in significant clinical improvement to the treatment of cardiac arrest patients. While we have previously demonstrated the feasibility of such artifact removal using a multichannel Wiener filter, we here focus on an efficient matching pursuit-like approach making practical real-time implementations of such a scheme feasible for a wide variety of sampling rates and filter lengths. Using more realistic data than what have been previously available, we present evidence showing the excellent performance of our approach and quantify its computational complexity.

Algorithms↗