Detecting Sustained Attention during Cognitive Work using Heart Rate Variability Cho-Yan Chen DEPT of Electrical Engineering University of National Cheng Kung Tainan, Taiwan
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Chi-Jen Wang E-Liang Chen Chi-Keng Wu DEPT of Nursing College of Medicine DEPT of CSIE DEPT of EE University of National Cheng Kung University of Chu-Te University of NCKU Tainan, Taiwan Kaohsiung, Taiwan Tainan, Taiwan
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Yen Kuang Yang Jeen-Shing Wang Pau-Choo Chung Dept of Psychiatry DEPT of Electrical Engineering DEPT of Electrical Engineering National Cheng Kung University Hospital University of National Cheng Kung University of National Cheng Kung Tainan, Taiwan Tainan, Taiwan Tainan, Taiwan
[email protected] [email protected] [email protected] heart rate, blood pressure and breathing rate decreased to Abstract—Sustained attention is an important have the body relaxed[1]. HRV can reflect various requirement when we are doing vigilant works. The physiological states such as stress, mental workload and detection of whether a worker is in sustained attention sustained attention on task. Porges and Raskin(1969) stage is important to maintain the safety of the worker. discussed that HRV was reduced during sustained attention. Thus this paper performs classification of sustained Because of these reason, it has been reported that whether a attention and non sustained attention phases based on the person is in sustained attention or relaxed situation will heart rate variability (HRV). To achieve this purpose response on the heart rate variability change [2]. several features are derived from time domain, frequency domain and nonlinear analysis from Electrocardiogram Based on this consideration, this paper proposes an (ECG). Then linear discriminant analysis (LDA) with Kapproach to estimate whether a person is in attention or non nearest neighbor (KNN) is adopted as the classifier. It was attention phase based on the heart rate variability change. In found that the proposed method is promising in classifying this approach several features are extracted from heart rate the sustained attention and non sustained attention with variability and the results are applied to a LDA combined 98% of accuracy. with sequential forward selection (SFS) feature selection HRV, Sustained attention,Classification method for classification. I.
INTRODUCTION
Sustained attention is the ability of focus on some stimuli during cognitive task. It is an important requirement for vigilant work in order to avoid the serious accident. The sustained attention is also an essential requirement for efficiently learning. As such, understanding whether a person is in sustained attention is important to maintaining the worker in a safety and efficient working condition. So detecting whether a person is in sustained attention is important for learning and work safety. In psychology cognition, DeGangi and Porges (1990) classify the attention progressing into 3 stages: attention getting, attention holding, and attention releasing that each stage demand attention level is different and sustained attention equal to the attention holding stage. HRV is calculated by beat-to-beat (RR interval) in heart rate and is controlled by the autonomic nervous system (ANS). ANS is divided into two parts: sympathetic and parasympathetic nervous system. When we meet stress, fear, and emergency status the sympathetic nervous will be active that will cause the heart rate, blood pressure and breathing rate increase to deal with these status. When we are in relax status the parasympathetic will be active that causes the
The remaining of the paper is organized as follows. In Section II we give a brief overview of the procedure of the experiment. Section III introduces the methods of the feature extractions and the classification method. Section IV shows the significant Poincare plot pattern. Section V shows the results of the experiment. Finally conclusions are drawn in Section VI. II.
EXPERIMENT
Twenty eight subjects, age (18-24), from the NCKU college join the experiment. We request the subject not to stay up late or drink caffeine drink before experiment in order to get the normal experimental data. Each subject wears the MY-ECG equipment with sample rate 500HZ to record the continuous ECG signal in real time. Each experiment is lasted 35 minutes involving baseline 10 minutes, attention task 15 minutes and rest 10 minutes. To induce attention, each subject is requested to perform the continuous performance task (CPT), which is a psychological task widely used for sustained attention/vigilance measurement. During the procedure of the CPT alphabets will be randomly chosen to be displayed for 250ms on the screen, and the subject needs to push the
mouse when the screen appears non-X alphabet. The time interval between every two consecutive alphabets is 2 seconds. The omission error represents the continuous attention index. The experiment is conducted in a quiet room in order to prevent the subject being affected. After the task we check the omission rate for each subject, and those of high omission rates will be removed from our analysis in order to avoid outliers [3]. III.
METHOD
The overall structure of our procedure is illustrated in Fig 1. Initially the preprocessing state is the QRS detection in order to get the RR interval. After we use the RR interval to calculated sixteen features involved time domain, frequency domain and nonlinear analysis then we normalized the features. For feature selection, we use the SFS to select the features and the Criterion Function (CF) is KNN. Finally we get the significant features from SFS and use it to classify the difference classes. The classifier we use the LDA and KNN to get the classification accuracy. Preprocessing (QRS DETECT)
Feature Calculation
Classification (LDA+KNN)
In the frequency domain of the HRV time series there are three frequency bands: the very low frequency (VLF) band (0.003~0.04Hz), the low frequency (LF) band (0.04-0.15Hz) and the high frequency (HF) band (0.15-0.4Hz) which carry significant meaning in medical research. Fig 3 shows one example of the three frequency bands. According to literatures [5] the parasympathetic activity dominates at HF, the sympathetic activity dominates at LF and the LF/HF which is the ratio between LF and HF, discriminates sympathetic effects from parasympathetic effects. We also calculated the normalized LF and HF, denoted as TP 、LFnu and LFnu, as follows: (1) TP VLF LF HF LF TP VLF HF HFnu TP VLF
LFnu
(2) (3)
Feature Normalization
Feature selection (SFS+KNN)
Fig. 1 The procedure of the analysis method
A. Preprocessing In order to extract the features we need to calculate the RR interval. The beat-to-beat (RR interval) is calculated by A Real-Time QRS Detection Algorithm [4] and its main procedure of this method is involving band pass filter, difference, square, moving window and adaptive threshold. Fig 2(a) shows one example of the detected R peaks. By calculating the peak differences we will obtain the RR intervals, as shown in Figure2 (b).Then the RR intervals are used for calculating the features.
Fig. 3 Power spectrum of the HRV showing the VLF, LF and HF.
In the nonlinear analysis we use features extracted from Poincare plot and the entropy. The Poincare plot displays the correlation between two consecutive RR intervals RR (n) on x-axis and RR (n+1) on y-axis where n is the data index in the sequence of RR intervals, as shown in Fig 4. If we perform a 45 degree transformation on the coordinate system to get the new axis x1 and x2 , the Poincare plot is related to the new system as follows: x1 cos x2 sin
sin RRn = 45 cos RRn 1
(4)
Fig. 4 Poincare plot of the HRV time series.
(a)
(b)
Fig. 2 Computation of RR intervals from ECG. (a) R peaks * (b) RR interval.
B. Feature calculation In the time domain of the HRV time series, we calculated four features including the standard deviation of RR intervals (SDNN), root mean square of successive difference RR interval (RMSSD), the number of successive RR intervals differing more than 50ms (NN50) and the NN50 count divided by the total number of all RR intervals (PNN50).
Let SD1 and SD2 be the standard derivations of x1 and x2 , respectively, and SD12 be SD1 divided by SD2. Based on such definition SD1 represents short term beat-to-beat variability and SD2 represents the long term beat-to-beat variability [6]. According to literature [7], the measurement of SD1 indicates the parasympathetic activity, the SD2 indicates the sympathetic effects from parasympathetic effects and the SD12 indicates the sympathetic activity. The approximation entropy (ApEn) is to measure the complexity and irregularity of the HRV time series. The bigger value of ApEn represents the more irregular of RR intervals and the smaller value of ApEn represents the more
regular of RR intervals[8]. The approximation entropy is calculated by the following steps. Let the vector U contains the RR intervals; that is, U [u(1),........u( N )] where N is the total number of RR intervals. Also let the vector x(i ) , i=1, …, N-m+1, contain the sub-sequence RR intervals of length m starting from the i-th interval, that is, x(i ) [u (i ),.......u (i m 1)] . For a given vector x(i ) , the
x(i) and another vector x( j ) , d [ x(i), x( j )] is defined as the maximum absolute difference between respective scalar components of x( j ) and x(i) , as follows: d m [ x (i ), x ( j )] max[| u (i k ) u ( j k ) |], k 0,1..m 1 (5) distance
between
Let d m i ( r ) represent the totally number of vectors x( j ) , j=1….N-m+1, with d m [ x (i ), x ( j )]
