Development of a Vital Sign Data Mining System for Chronic Patient Monitoring

Transcription

1 Development of a Vital Sign Data Mining System for Chronic Patient Monitoring Vincent S. Tseng 1 Lee-Cheng Chen 1 Chao-Hui Lee 1 Jin-Shang Wu 2 Yu-Chia, Hsu 3 1 Dept. of Computer Science and Information Engineering, National Cheng Kung University, Taiwan, R.O.C. 2 Dept. of Family Medicine, National Cheng Kung University Hospital, Taiwan, R.O.C. 3 Southern Innovation Center, Institute for Information Industry, Taiwan, R.O.C. tsengsm@mail.ncku.edu.tw Abstract In recent years, the structure of global population keeps going towards highly-aged continuously. The development of chronic patient medical care system becomes important and meaningful since people paid a lot attention to medical prevention. The medical care system has to provide alerts in time before the severe chronic illness occurs, such as stroke, diabetics, heart disease. Thus, necessary procedures can be taken in short time to save one precious life. In this paper, we presented a data mining system for chronic patient monitoring with applications on caring of cardiovascular patients. By mining vital signs like ECG, the system can predict with a classification tree and inform doctors to take actions if any anomaly could happen. A series of experiments on PAF data showed that our system can stably predict the anomaly from patients ECG data without coding of medical rules as done in other existing approaches. Keyword: Data mining, Electrocardiogram analysis, Patient monitoring system, Vital sign analysis 1. Introduction With the development of global civilization and industry, the structure of global population keeps going towards highly-aged continuously. It makes the old-aged medical care more important. If we can take notice on patient s vital signs like electrocardiogram (ECG), blood pressure, heart beat and body temperature, it is very likely that we can make appropriate care procedure on the patients in time. Therefore, it is valuable to develop medical care and monitoring techniques. The main function of a medical monitoring system is to receive the vital signs of patients to monitor their status. Under the procedure of monitoring, it can provide alerts and proper medical decisions to paramedics based on the collected data. Figure 1 shows the workflow for patient vital sign monitoring. Our goal is focused on developing a data mining system for analyzing the vital signs of chronic patients. For applications, we treated illness related to ECG as application since ECG has been proved effective in clinical medicine for years. We sought for doctors professional opinions since the signatures and diagnoses already had explicit directions. Moreover, we proposed a chronic illness predicting mechanism to explore the relation between bio-signals and illness changes. Therefore, prediction model building could be applied to various chronic illnesses corresponding to patients bio-signals. For a period of ECG, we allocate P wave, QRS complex and T wave with medical knowledge. The period of each waveform has a static rhythm, as shown in Figure 2. Lab Sample Life Support Equipment Patient Bio-monitoring System Signal 病 Patient 人 Pharmacy Receive Data Diagnosis Treatment Lab Result Equipment Setting Doctor Nurse Figure 1. Workflow for patient vital sign monitoring. The structure of this paper is organized as follows: We discuss related works in Section 2, and section 3 gives a detail description of the vital sign monitoring system we proposed. Section 4 describes the experiments and performance evaluation. Finally, we make a conclusion and future work in section 5. R P Q T S Figure 2. A rhythm map of ECG waveform. -1-

2 2. Related Work The traditional patient monitoring system was proposed by Puers [8] in Its structure contains data receiving, transmission, paramedics and functional components. The main part of this structure is to process patients vital signs by equipments and send to paramedics and expert system to make diagnoses. Alonso et al. [1] [8] proposed a leading system as shown in Figure 3. Bio-signal time series were transformed by Fourier Transform and indexed by R* Tree. It could introduce a general model and rule set to treatment unit. DataBase Step Step 1 Step Step 2 Step Step 3 Using the discrete Creating R* tree Fourier transform classification Creating reference model for popular groups Support Action Treatment RuleBase Step Step 4 Suggestions Figure 3. Structure of patient monitoring as proposed by Alonso et al. There are some related studies to bio-signal analysis, such as ECG analysis and parsing. For instance, Wolff el al. [4] used a non-supervised to diagnose via bio-information, or detect apnea by ECG [6] [7]. Even more, the complete hardware equipment [5] applied to daily life care. The above methods were traditional statistics basically. They could achieve certain results, yet in tremendous data the precision and time cost performed relatively lower. W. Zong [11] pointed out that Paroxysmal Atrial Fibrillation (PAF) is related to the occurrence of Atrial Premature Contraction (APC) and the closer to the point of PAF, the more impact of APC. For this kind of approach, some medical rules have to be set in advance. This might not be feasible in some applications, e.g., analysis of new diseases. Therefore, we presented a general data mining system for patient monitoring in this paper without requirements of coding medical rules in advance. 3. Patient Monitoring System 3.1 Problem Definition We assumed that patients illnesses are often related to some kinds of their bio-signals. By observing those bio-signals and adopting corresponding medical attributes identified by professional doctors, we are able to build a classification tree based on target illness with data mining methods. Thus doctors could take suggestions of the system into account and diagnose more properly. 3.2 System Flow Figure 4 shows the flow of patient monitoring system proposed in this study. The flow could be separated into several parts as described in the following. Firstly, the system caught patient s raw bio-signals. For training data, a diagnosis would be given, such as normal or abnormal. We then derive some bio-indicators through the doctors domain knowledge based on the illness. With the stage, the rule s reliability and precision could be enhanced. With these indicators, we transformed raw bio-signals into an attribute table and performed data mining skills to generate a classification tree. Finally, this tree could be applied to clinical diagnoses. The next paragraph will describe the realization of the system. Raw bio-signal (i.e. ECG) Experts identified key indicators Extract value from raw bio-signal Perform data mining on all attribute Generate classification rule Diagnosis will be given in train set i.e.:heart rate,count of APC Output Figure 4. Flow of patient monitoring system. 3.3 The realization of patient monitoring system Since raw bio-signal data are usually consecutive time series sequences, we identified some medical reference points by tools provided from WFDB [12]. Then, we preprocessed signals for easier reading thereafter through bio-indicators recommended by doctors. Because patients bio-signals have individual differences, we proposed a mechanism to numerate and emphasize the attributes which doctors recommended in order to simplify the construction of classification model. Figure 5 shows the idea of the extreme pattern statistics we proposed. This method normalized raw bio-signals first and then treated each attribute as a distribution. We divided this distribution into 100 parts between the maximum and minimum and set a threshold parameter α. Then we extracted α percent data of both side endpoints and calculated their origin average and standard deviation to fully record the characteristic of this attribute. We did the above procedure for every attribute that doctors recommended and recorded the distribution of characteristic such as the number of anomaly and average. We set a parameter β for processing simplicity. The parameter β is for dividing the whole bio-signal as β segments to perform easier on time series. It contains the same attributes in each segment. A weight of a segment would be given then after considering the importance of medical theory, doctors recommendation and the time impact of attributes. For example, the doctors -2-

3 recommended attributes as {a 1,a 2, a 3,, a k } for an illness and we divided a bio-signal into segments of {S 1,S 2, S 3,, S β }. Thus, every segment S n contains attributes {a n1, a n2, a n3,, a nk }. We assume the weight of S n is W n, and we calculated extended attributes a j, as shown below. β ' j i, j i i= 1 a = a W,1 j k By applying the above procedure, we could have a set of attributes as {a xy 1 x β, 1 y k} {a j 1 j k}. Thus the above data could serve as training data in building the classification model. Table 1 shows the table for numerical attributes after preprocessing. In order to focus on various data and illness more preciously, we adopted a multi-classifiers method to build classification model. F-measure was used to evaluate all classifiers in this kernel as shown below: 2 precision 2 TP F measure = = + precision 2 TP + FP + FN The evaluation of F-measure takes and precision into consideration to prevent a high and high false positive situation. Based on known data, we can choose the classifier with the highest F-measure value in multi-classifiers for further anomaly detection and diagnosis kernel, as shown in Figure 6. With the previous built classification model, we could give the preprocessed raw bio-signals into this model and decide whether this case is normal or at risk. Occurrence The whole value Abnormal: α% we divided ECG into 3 segments, that is, β=3. Data Table Classifier 1 Model 1 Classifier 2 Model 2 Classifier N Model N Figure 6. Multi-classifiers evaluation. Classification Rule Table 1. Example data after preprocessing. Case 1 Case m Case m+1 Case n Attribute A Attribute B Attribute X Diagnosis Value A 1 Value A 1 Value X 1 Normal Normal Abnormal Abnormal A classification result is shown in Table 3. The indicators for evaluating the system are mainly precision and and aided with false positive (FP), as follows: X + W precision =, X + Y + Z + W Y FP = Y + W W = Z + W Table 2. PAF dataset. Train sets Test sets Normal Patients Non-Immediate Risk (No PAF 45minutes later PAF or former) Patients Patient with PAF in minutes later Minima Normal part: 100-2α% Value Maxima Table 3. Diagram of a classification result. Prediction Class Normal Abnormal Normal X Y Abnormal Z W Figure 5. Diagram of extreme pattern statistics. 4. Experimental Evaluation 4.1 Experiment Dataset The bio-signals used in the following experiments were obtained from PhysioNet [15]. From this source, we chose the PAF Database [13] for our experiment samples which are ECG signals in 30 minute long. The dataset includes 100 training instances, which have 50 normal patients and 100 test instances, as shown in Table 2. This study mainly targeted on the patients with PAF. Based on suggestions from a medical expert, 4.2 Experimental Evaluation Extreme Pattern Statistics We tested different settings of α in extreme pattern statistics for classification. The training sets were taken to do the inner test. Figure 7 shows the experimental results. When α =95 or 90, the precision outperformed others. Besides, the under α=90 is better than that under α=95. Thus, we set α=90 for our further experiments Evaluation of Multi-classifiers -3-

4 We used different kinds of classifiers provided by WEKA [3], and the evaluation results are shown in Table 4. Obviously NaiveBayes could achieve 68% of. Besides this, we chose J48 classifier for the following experiments since it generates a rule-based classifier rapidly. Based on the above, we adopted NaiveBayes and J48 to build multi-classifiers of the system. 100% 80% 60% 40% 20% 0% 40% 50% 60% 70% 80% 90% 95% α% Figure 7. Extreme pattern statistics Effects of number of Attributes According to medical expert s recommendations, we experimented two sets of attributes, shown as follows (avg abbreviates for average and stdev for standard deviation): Set A avg, stdev of P-wave length s difference avg, stdev of P-wave amplitude s difference avg, stdev of R-wave length s difference avg, stdev of T-wave length s difference avg, stdev of T-wave amplitude s difference count, avg, stdev of time lag between T and P-wave count,avg,stdev of time lag between P and Q-wave weighted attributes of the above Set B count of P-wave length difference count of P-wave amplitude difference count of R-wave length difference count of T-wave length difference count of T-wave amplitude difference weighted attributes of the above Set B enhanced the attributes of Set A and included weighted attributes as well. The result is shown in Table 5. We compared those two combinations of attribute sets mainly in the NaiveBayes classifier. Set B had better than Set A and reduced false positive as well Effects of Weighting Precision Recall In previous experiments, we introduced a weight mechanism on 3 segments. According to the studies of references and the doctor s recommendation, we gave more weight for the segment near the endpoints of data. We tested certain kinds of weight methods and their value: linear weight <1,2,3> square weight <1,2,4> square weight <1,3,9> square weight <1,5,25> We chose 5-Fold validation method to repeatedly test upon the above weight policies, shown in Table 6. In aspect of, linear weight <1,2,3> was less than square weight <1,2,4> and <1,3,9>, yet square weight <1,3,9> and <1,5,25> were similar to square weight <1,2,4>. In another aspect of precision, all weight skills performed the same. Briefly speaking, square weight <1, 2, 4> was better since we emphasized on the outcome of. Table 4. Multi-classifiers experiment. false positive NaiveBayesSimple 68% 67% NaiveBayes 68% 69% libsvm 61% 76% Radial Basis Function 57% 56% Random-ForestTree 39% 21% J48 32% 26% 1-NN Classification 29% 26% NaiveBayes-Multi 29% 36% LogitRegression Tree 25% 36% Classification Tree 21% 19% Probit-Regression Tree 21% 35% ADTree 18% 25% RandomForest 7% 4% Table 5. Comparison of number of attributes. NaiveBayes NaiveBayes J48 false positive Set A 67% 69% 32% Set B 75% 55% 28% Table 6. Comparison of weight experiment. precision <1,2,3> 56.32% 63.14% <1,2,4> 54.64% 68.97% <1,3,9> 56.32% 69.00% <1,5,25> 57.76% 57.42% Effects of the Length of Bio-signals Currently we experimented with 30 minutes ECG and we discuss on different lengths of bio-signals in this paragraph. We extracted the beginning 5 minutes, 15 minutes, and the whole 30 minutes in the ECG data and made an experiment shown in Table 7.In the aspect of, 30 minutes ECG data performed more stable than any other time sample. However, the sample of 5-4-

5 minutes ECG could achieve 61.48% of precision and relatively low 46% of. We inferred that the normal patterns are more obvious in short time and the abnormal patterns need a long observation. The length of bio-signals had not much impact on precision in this experiment, but longer bio-signals definitely are good for the diagnoses of illnesses. Table 7. Effects of bio-signals length. precision 5 minutes 61.48% 46% 15 minutes 54.52% 59.71% 30 minutes 54.64% 68.97% 4.3 General Experimental Result We showed the adjustment on parameters and data types through the above experiments. For PAF, the whole setting and experiment results are described as follows: 1. We got 30 minutes ECG in PAF and set β=3 to divide data into 3 segments. 2. By medical expert s recommendation in section 4.2.3, we extracted each attribute with respect to every segment. 3. We transformed data by using extreme pattern statistics and set α=90 to get the attribute table such as Table We extended the attributes with a weight <1,2,4> in the attribute table 5. Finally the multi-classifiers read the preprocessed attribute table to build PAF classification model. We evaluated J48 and NaiveBayes classifier via F-measure and chose better one for the classification kernel. 6. We tested performance by 5-Fold validation. The experiment showed that this system could achieve 54% of precision and 68.97% of. In the PhysioNet 2001 Challenge Event 2 (PAF Prediction) [14], the was ranged between 54% and 79%. The system of this research can achieve stable 68.97% of. 5. Conclusion and Future Work We have presented a data mining system for chronic patient monitoring with applications on caring of cardiovascular patients. By the operation of this system, we could aid general and chronic illnesses diagnoses. Based on the designed architecture, we completed an analysis and prediction system on chronic illnesses via ECG. This system could do analysis and prediction to assist the care and diagnoses in clinical medical related to ECG. The followings are the main characteristics of the system: 1. A foundation based on light medical knowledge: This system was not a fully independent auto system. We still required experts to list the features of bio-signals. This mechanism could avoid irrelevant information to affect the system and ensure the medical basis of the results. However, only light medical knowledge instead of precise medical rules is needed in our system. 2. Feature extraction in periodic data: This system adopted segmentation skill and extreme pattern statistics. Segmentation skill could avoid the missing impact of relatively infrequent patterns to data mining and emphasize the signal signature in the period of time. Extreme pattern statistics could calculate the changes of attributes in each segment and make a great contribution to illness data analysis and mining. 3. Adaptive structure to data signatures: Our structure has considered medical factors with various bio-signals. Therefore, the operation of this system did not adapt to a kind of signal only. The embedded multi-classifiers could improve the flexibility of data requirements. 4. Stable analysis results: Based on real data experiments, we observed that system could have a certain precision and to illness prediction. For public test data PAF, the system could maintain stable sensitivity and the result was not far from the achievement of medical approval. This shows that the system had stable results in bio-signal analysis and illness prediction. Our research had completed a system design and requirement for chronic patient bio-signal data mining techniques and validated the possibility by real data. Currently we applied the system to illness related to ECG only. For the future work, we pointed out some possible aspects here: 1. The reduction of parameters: Besides the doctors recommendation, this system still had to set three parameters. We would like to reduce the number of parameters or provide a parameter-free system. 2. The application of bio-signals: This system took ECG into consideration for now. We would hope to combine time series, such as blood pressure and breathe, to enhance the analysis and the feasibility with kinds of illness. 3. The choice of multi-classifiers: This system adopted a measuring indicator to use a better classifier only. We would like to combine the results of multi- classifiers to achieve a higher performance. Acknowledgement This research was supported by the Applied Information Services Development & Integration project of Institute for Information Industry and sponsored by MOEA, Taiwan, R.O.C. under grant no IA95H01311D01. References [1] F. Alonso, J. P. Caraca-Valente, L. Martinez, C. Montes, Discovering similar patterns for characterizing time series in a medical domain, Proceedings IEEE International Conference on Data Mining, 2001, pp [2] N. Friedman, D. Geiger, M. Goldszmidt, Bayesian Network Classifiers, Machine Learning, vol. 29, -5-

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