Signal Quality Assessment And Analysis Of Vital Signs In Critical Care

Modern intensive care units (ICU) employ an impressive array of technologically sophisticated instruments to provide detailed measurements of the physiological and pathophysiologic state of each patient. But the physiological signals in the ICU are often severely corrupted by noise, artifact and missing data, which lead to large errors in the estimation of the signals values. This can result in a high incidence of false alarms from ICU monitors, which can sometimes be as high as 86% for some alarm types. Frequent false alarms due to data corruption will lead not only to sleep deprivation for patients and stress for patients and staff, but also to wasted time, resources, and to a desensitization of clinical staff to real alarms and a consequent drop in the overall level of care.This paper started with the signal quality assessment of vital signs in intensive care patients, derived the signal quality index (SQI) to reveal the degree of signal quality. Based on the SQI, the vital signs were estimated in the presence of high levels of noise and artifact. And then the arrhythmia false alarm reduction algorithm in ICU monitors was accomplished.The methods of signal quality assessment for electrocardiogram (ECG) and arterial blood pressure (ABP) were studied. Since different ECG beat (QRS) detection algorithms are sensitive to different types of noise, a novel idea was presented that the signal quality can be reflected by comparing the accuracy of different QRS detectors. The SQI of ECG signals was obtained by combining four analysis methods: the comparison of multiple beat detection algorithms, the comparison of different synchronous ECG leads, evaluation of the kurtosis (randomness) of an ECG segment and calculating the proportion of the spectral distribution of a given ECG segment. The SQI of ABP signals was obtained by a combination of two algorithms: a beat-by-beat fuzzy logic-based assessment of features in the ABP waveform and heuristic constraints of each ABP pulse to determine normality. The SQI ranges between 0 and 1 inclusively. High value of SQI means good quality and low value means bad quality.After obtaining the SQI, we studied the estimation of the vital signs, such as heart rate (HR) and blood pressure, in the presence of high levels of noise and artifact. The HR directly obtained from the beat detection of ECG or ABP and the blood pressure directed obtained from ABP feature extraction are easily corrupted by noise. The Kalman filter is an optimal state estimation method for stochastic signals and can minimize the estimation error caused by noise. We studied the algorithms of HR and blood pressure estimation based on Kalman filter and proposed the idea to optimize the Kalman filter by adjusting the gain of Kalman filter according to the SQI. When the SQI is high, we elevate the Kalman gain and force the Kalman filter to trust the current measurement and use the current measurement to adjust the system state. When the SQI is low, we depress the Kalman gain and force the Kalman filter to trust the current measurement less. Furthermore, an upper limit that defines the cusp between good and bad data is defined. When SQI is lower than the upper limit, the KF is not updated. So it will escape the severe influence of noise when SQI is too low.HR information can be obtained easily by beat detection from the ECG, ABP and pulse oximetry waveforms. These sources provide approximately redundant and independent measures of HR. Furthermore, the sources of the noise and artifact are often weakly or uncorrelated with both the signals that are cardiovascular in origin, and with each other. Reliable estimation of HR can therefore be obtained by sensor fusion. We studied the data fusion algorithm to obtain the optimal estimation from multi noisy signal sources. Then we realized the HR data fusing estimation based on the SQI and the innovation (residual error) of Kalman filter. We presented the novel idea that the data fusion can be weighted by SQI and the innovation of Kalman filter. When one channel is corrupted by artifact and the HR from this channel is miscalculated, the SQI will be low and the sudden change of HR will make the residual error large. So the weight for this channel will be set to a small value. Data fusion method can provide robust cardiovascular parameter estimates even when only one channel of data is relatively noise free.After obtained the fusing HR, the arrhythmia false alarm reduction algorithm was performed based on the fusing HR and SQI. When the monitor alarm was set, we compared the fusing HR with the threshold of the monitor and made the decision of accepting or rejecting the alarm based on SQI. If the fusing HR did not exceed the threshold of the monitor and the SQI was high enough, we believed in the fusing HR estimation and suppressed the false alarm.To evaluate the algorithms in this paper, we established a noise free dataset, named clean dataset, by selecting the noise free data from the Multi-Parameter Intelligent Monitoring for Intensive Care II (MIMIC II) database. The clean dataset included 437 subjects, comprising 3762 1 h or longer (1.62±0.69 h) data segments or 6084 h in total, with the ABP and at least one channel of ECG simultaneously present. The ECG and ABP evaluation dataset were established by adding ECG and ABP noise with different types and intensities to the clean dataset. The ECG noise sourced from MIT standard ECG noise database (NSTDB), including 3 types and 6 different signal-noise-ratio noises. We created models of ABP noise, including 6 types and 5 different noise percentages. The false alarm suppression evaluation dataset included a subset of over 5344 life-threatening arrhythmia alarms taken from the MIMIC II database and was annotated by experts. The false alarm rate of the dataset is 42.74%.To evaluate the HR estimation algorithms, we obtained the golden standard HR which can be applied to evaluate the methods under noisy conditions. We evaluated eight different methods of HR estimation and three methods of blood pressure estimation. The results show that the ECG and ABP SQIs of evaluation dataset decrease along with the increase of noise. The SQI reflects the degrees of noise in physiological signals effectively. The SQI plays an important role in eliminating the effect of noise and artifact from HR estimation. It is evident that when there is no SQI control, the error is much higher when the SQI is low (and the noise level is high). The Kalman filter and SQI based estimation algorithms give good HR and blood pressure estimations. The data fusion algorithm performs better. The root mean squared error (RMSE) of the fusing HR estimation is less than 1 beat/min when high levels of ECG noise exist. And the RMSE is less than 2.1 beats/min in the presence of high levels of ABP noise. The blood pressure estimation algorithm can suppress the estimation error differently accord to the sensitivity of SQI to noise types. The results of false alarm reduction algorithm show that the false alarm reduction rate was 53.68%, and the corresponding true alarm acceptance rate was 100.00% for extreme arrhythmia alarms. The false alarm rate decreases from 42.74% to 19.80%.The sound vital signs estimation and the signal quality assessment are the base of intelligent monitoring. Only when the correct and reliable data were obtained, the physiological and pathophysiologic state of patient can be interpreted, predicted and assessed correctly. This paper still has some shortcomings. The SQI of ABP is not sensitive to some types of noise. This paper only studied ECG and ABP signals. The false alarm reduction algorithm only dealt with arrhythmia alarms. We would do more detail researches on other vital signs and other types of false alarm reduction later.