identification of ischemic heart disease via machine learning analysis on magnetocardiograms
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Computers in Biology and Medicine 38 (2008) 817825www.intl.elsevierhealth.com/journals/cobm
Identication of ischemic heart disease viamachine learning analysis onmagnetocardiograms
Tanawut Tantimongcolwata, Thanakorn Naennab, Chartchalerm Isarankura-Na-Ayudhyaa,Mark J. Embrechtsc, Virapong Prachayasittikula,
aDepartment of Clinical Microbiology, Faculty of Medical Technology, Mahidol University, 2 Prannok Road, Bangkok-noi, Bangkok 10700, ThailandbDepartment of Industrial Engineering, Faculty of Engineering, Mahidol University, Nakhonpathom 73170, Thailand
cDepartment of Decision Sciences and Engineering Systems, Rensselaer Polytechnic Institute, Troy, NY, USAReceived 10 May 2006; accepted 24 April 2008
Ischemic heart disease (IHD) is predominantly the leading cause of death worldwide. Early detection of IHD may effectively prevent severityand reduce mortality rate. Recently, magnetocardiography (MCG) has been developed for the detection of heart malfunction. Although MCGis capable of monitoring the abnormal patterns of magnetic eld as emitted by physiologically defective heart, data interpretation is time-consuming and requires highly trained professional. Hence, we propose an automatic method for the interpretation of IHD pattern of MCGrecordings using machine learning approaches. Two types of machine learning techniques, namely back-propagation neural network (BNN) anddirect kernel self-organizing map (DK-SOM), were applied to explore the IHD pattern recorded by MCG. Data sets were obtained by sequentialmeasurement of magnetic eld emitted by cardiac muscle of 125 individuals. Data were divided into training set and testing set of 74 cases and51 cases, respectively. Predictive performance was obtained by both machine learning approaches. The BNN exhibited sensitivity of 89.7%,specicity of 54.5% and accuracy of 74.5%, while the DK-SOM provided relatively higher prediction performance with a sensitivity, specicityand accuracy of 86.2%, 72.7% and 80.4%, respectively. This nding suggests a high potential of applying machine learning approaches forhigh-throughput detection of IHD from MCG data. 2008 Elsevier Ltd. All rights reserved.
Keywords: Ischemia; Magnetocardiography; Machine learning; Back-propagation neural network; Self-organizing map
Heart diseases are the leading cause of death worldwide.Heart malfunction usually rises from ischemia due to inade-quate blood supply to the cardiac muscles. Long-term de-ciency of oxygen and nutrients of heart musculature leads todamage of cardiac tissue which ultimately culminates in heartattack and death. Early diagnosis of heart ischemia is criticalto help reduce the mortality rate.
Electrocardiography (ECG) is traditionally used to monitorischemic heart disease (IHD). It demonstrates the electrophys-iological activity of heart via electrodes attached on the torsosurface. However, in some circumstances, ECG may be normal
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in patients at rest during angina. Various advanced methodshave been developed to improve diagnostic performance of car-diac abnormalities, such as myocardial perfusion scintigraphyand cardiac catheterization. Nonetheless, these techniques areinvasive, time-consuming and require technical experts for op-eration. Therefore, a non-invasive and contact-free technique,namely magnetocardiography (MCG), has been developed.
MCG is a sensitive technique that monitors the magneticeld emitted by cardiac tissues. It has been considered as apromising alternative to traditional ECG since superconductingquantum interference device (SQUID) is used as contact-freeprobe . This provides high reproducibility, less signaluctuation and better spatial resolution over ECG . MCGhas been proven to be an efcient tool for many clinical appli-cations. It is useful to monitor fetal heart activities and showssuperior efciency to determine myocardial infarction over
818 T. Tantimongcolwat et al. / Computers in Biology and Medicine 38 (2008) 817825
ECG [5,811]. Using MCG, coronary artery disease can poten-tially be detected in patient presenting acute chest pain .Three-dimensional localization of myocardial lesion can also bevisualized by magnetic mapping of MCG . With fore-mentioned, interpretation of MCG remains a challenge since itrequires highly experienced personnel and time-consuming.
Machine learning is a promising technique that explorespreviously unknown regularities and trends from diverse datasets by learning from sets of data and extrapolating the new-found knowledge on testing data . This technique haswidely been adopted for biomedical applications. Lympho-cytic leukemia has successfully been identied using automaticmorphological analysis via machine learning [19,20]. Normal,sickle or other abnormalities of erythrocytes can be classied bythe combination of image feature analysis and machine learningprocedures . Furthermore, successful recognition of splicejunction sites of human DNA sequences has been achieved viaself-organizing map (SOM), the BNN and support vector ma-chine (SVM) learning methods .
Recently, the machine learning has been applied as a toolfor detection of cardiac diseases. Classication of arrhythmiafrom ECG data set has successfully been performed by OneR,J48 and Nave Bayes learning algorithm [22,23]. Analysis ofmyocardial infarction can be achieved by SOM, the BNN androbust Bayesian classiers [24,25]. As mentioned before, MCGprovides better discriminating power between healthy and dis-ease cases. Therefore, attempts at application of machine learn-ing have preliminarily been performed with various algorithmson MCG by our group .
Herein, two machine learning methods, namely back-propagation neural network (BNN) and direct kernel self-organizing map (DK-SOM), have been demonstrated as repre-sentative of supervised and unsupervised learning techniques.Supervised learning involves learning by example pattern,while the unsupervised approach learns by discovering andadapting to structured features in the input patterns . There-fore, we aim to demonstrate improved usage of the DK-SOMalong with application of the BNN as tools for concurrentidentication of myocardial ischemia from MCG.
2. Materials and methods
2.1. Subjects and data acquisition
MCG patterns were taken from 125 individuals. Among this,55 patterns were from patients who had been clinically evalu-ated having myocardial ischemia. The remaining 70 individu-als were healthy and show no evidences of cardiac abnormalsymptom. The data from both patient and healthy subjects wererandomly selected as training set (74 cases) and testing set (51cases).
MCG data were acquired from 36 locations above the torsoby making four sequential measurements in mutually adjacentpositions. At each position, the cardiac magnetic eld wasrecorded by nine sensors for 90 s using a sampling rate of1000Hz, resulting in 36 individual time series. For diagnosis ofischemia, MCG signal was recorded under default lter setting
at 0.05100Hz. The post-processing was performed by apply-ing digital low pass lter at 20Hz [28,29]. For automatic clas-sication, we used data from a time window between J-pointand T-peak (JT interval) of the cardiac cycle . The JT in-terval was subdivided into 32 evenly spaced points resulting ina set of 1152 descriptors from 36 MCG signals for each indi-vidual case. Therefore, the descriptors from 125 cases (testingand training) were used as input for IHD prediction (Fig. 1).
2.2. Overview of machine learning approachesThe BNN, also called multilayer perceptron, is a supervised
learning method, where feeding of example input patterns to-gether with target or output patterns is needed to train the learn-ing network. A general BNN hierarchical network is illustratedin Fig. 2. It is comprised of one input layer, one or more hiddenlayers, and one output layer. Each layer contains a processingunit namely neurons or nodes , where the data computationsand transformations take place. The connections among nodesof the layers are made for further processing and transferringof the computed data. The strength of the signal ow via eachconnection is expressed by a numerical value known as learn-ing weight, wherein the path of knowledge ow is discovered.The learning process starts with a projection of each trainingdata to each neuron in the input layer. The data are then trans-ferred to the neurons of the hidden layer under the control ofinitial weights of learning, which are randomly generated andseeded to the connections. Each training pattern is propagatedin a forward manner (layer-by-layer) until an output pattern iscomputed. Afterwards, the computed output is compared to thedesired or the target output and an error value is determined.As the name infers, back-propagation neural network, the er-rors are fed in a backward manner (layer-by-layer) to adjustthe synaptic weights of the learning network. The process isrepeated several times for each pattern in the training set untilthe total output error converges to a minimum or until a settinglimit is reached during the training iterations .
The DK-SOM is referred to as a traditional SOM appliedon kernel transformed data. Prior to processing through SOM,the input data are introduced to the kernel transformation pro-cedures. Kernel transformation is a class of pattern recognitionalgorithm, which searches for similarity among the features ofthe descriptors. In this study, the nonlinear characteristics ofMCG are captured and hidden within the kernel before beingsubmitted to the SOM. SOM is an un