Study Of Heart Rate Estimation Algorithm Based On Data Fuaion And Depression False Alarms Of Intensive Monitor

Physiological vital signals of intensive care unit (ICU) patients such as electrocardiogram(ECG) and arterial blood pressure (ABP) are the important indicators to physician's diagnosis and treatment for the patients. Nevertheless, these signs are often severely corrupted by noise, artifact and missing data, which lead to large errors in the estimation of signal parameters, keeping a high incidence of false alarms from ICU monitors, resulting in lack of trust of clinical staff to monitor alarming signals, neglecting the real critical information, greatly diminishing the effect of care. Currently heart rate estimation relies mostly on ECG analysis, the diagnosis in arrhythmias is closely related with heart rate, so the results are greatly influenced by the interference. This paper studies ECG and ABP at the same time and gives a comprehensive analysis and intelligent diagnosis of signals from two sources, suppressing false alarms of arrhythmia and increasing accuracy and sensitivity of the alarm monitoring system.Firstly, we acquired heart rate values respectively from ECG by using QRS signal waveform recognition algorithm, and from ABP by using pulse and blood pressure identification algorithm, followed by applying signal quality assessment algorithm to get signal quality index (SQI)of ECG and ABP. Signal quality index can provide the objective indicators in judging the signal's quality. Then, Kalman filters were applied in heart rate estimation one by one, and we adjusted Kalman filter gain coefficient adaptively through signal quality index. When the signal quality was low, we did not update the Kalman filter, keeping the heart rate estimation as the previous value, so as to avoid the serious effects of noise on heart rate estimation. Finally, the residual error of Kalman filter and signal quality index were applied as a weighting coefficient in data fusion of ECG and ABP, to calculate the fusing heart rate.We studied the algorithm that integrated fusing heart rate and signal quality index to inhibit serious bradycardia and tachycardia false alarms created by monitors. Firstly ECG and ABP data were used in heart rate integration estimations, and we judged the credibility of integrated HR according to the level of signal quality index in order to suppress false alarms. When the integrated heart rate we calculated did not exceed the alarming thresholds of heart rate set in monitor, and if the SQI of at least one signal (either ECG or ABP) was above 0. 5, we trusted the heart rate at this time and considered that it did not exceed the threshold and the alarm was false and to be suppressed, or else accepted the alarm. To evaluate our algorithm, we used the Multi-Parameter Intelligent Monitoring for Intensive Care (MIMIC) II database established by the Health Sciences and Technology of Massachusetts Institute of Technology Centre (MIT/HST). The ECG and ABP data recorded in the database according to 2432 severe tachycardia and bradycardia alarms produced by the monitor were reanalyzed using this suppression alarm algorithm. The result was compared with the expert group report notes one by one. The results show that the correct rate identifying true alarms using the algorithm is 99.64%, and the rate of depressing false alarms is 66.18%.Data fusion algorithm based on two-channel ECG and ABP signals can be extended to multi-channel data fusion, and to evaluate synthetically the physiological parameters of ICU patients. Next, we plan to improve the algorithm by combining ECG and blood pressure waveform morphology to analyze alarming signals of ventricular arrhythmia, so as to improve the quality of intensive care.