Migraine classification by machine learning with functional near-infrared spectroscopy during the mental arithmetic task | Scientific Reports -…

The experimental process can be divided into five steps, as shown in Fig.6. First, we need to recruit subjects to take a Mental Arithmetic Task (MAT). When doing the task, the blood oxygen information could be measured by the device. Secondly, in order to obtain more valuable signal, signal filtering is required. Afterwards, do the signal segmentation according to the three stages of the task and do the feature extraction. Eventually, the features can be imported into the machine learning. The details will be left to the following subsections for more explanations.

The experimental process of this study. After the fNIRS signal is obtained, it will be filtered, segmented and extracted and imported to machine learning to get classification results, and finally confirm the credibility of the results with cross-validation.

Nowadays, more and more techniques have been investigated to explore the relationship between migraine and cerebrovascular reactivity or cerebral hemodynamics. Some studies have used positron emission tomography (PET) to scan the prefrontal cortex (PFC) and assess whether the suboccipital stimulator is effective5. Others have found that the ventromedial prefrontal cortex is more active in MOH than CM subjects through functional magnetic resonance imaging (fMRI)6. Both PET and fMRI are non-invasive imaging modalities but the former requires the application of radioactive imaging agents which lead to the concern for ionizing radiation. Although the latter does not involve radioactive agents, the use of a strong magnetic field excludes patients with an artificial pacemaker or any metal implants.

As early as 2007, there was a study using near-infrared spectroscopy (NIRS) to evaluate the difference in regional cerebral blood flow (rCBF) changes of the middle cerebral artery between migraine patients and the healthy control group during the breath-holding task7. In recent years, NIRS has gradually emerged in the pain field8,9,10,11. Moreover, NIRS has the advantages of non-invasive, non-radioactive, instant, low system cost, portability and easy operability, etc. Therefore, NIRS has an extremely high potential as a tool for investigating migraine.

The continuous-wave NIRS system used in this experiment is a self-developed instrument in our laboratory, as shown in Fig.7. Optode is the core of the system, consisting of three light detectors and two near-infrared light emitters staggered with a spacing of 3 cm. The four channels of the system cover the PFC, approximately at the positions of F7, Fp1, Fp2, and F8 in the International 1020 system, shown in Fig.8. The photodetector uses OPT101 (Texas Instruments Inc), which has the advantages of small size and high sensitivity to the near-infrared light band. The multi-wavelength LEDs (Epitex Inc, L4*730/4*805/4*850-40Q96-I) contain three wavelengths of 730 nm, 805 nm, and 850 nm. In this study, we use only 730 nm and 850 nm. The sampling frequency is about 17 Hz. The rear end of the device is equipped with an adjustment knob, which can make the device fit properly, and reduce the influence of external light. The power supply of the hardware uses a rechargeable 7.4 V battery, composed of two 3.7 V lithium batteries in series, and is directly connected to the microcontroller unit (MCU), an Arduino Pro Mini. The other components (including light detectors, a Bluetooth module, and a current regulator) are powered by the output pin of the MCU. The current regulator uses TLC5916 (Texas Instruments Inc), which can provide a constant current for the LEDs in the circuit. The MCU converts the original light intensity signal into the hemoglobin value and sends these data back to the computer through Bluetooth for storage. Finally, the computer displays the hemoglobin value in real time.

The wearable functional near-infrared spectroscopy system. (a) OPT101 (b) LED (c) Power source (d) MCU (e) Bluetooth module (f) Regulator knob.

The Schematic positions of fNIRS optodes in the international 1020 system.

MAT is a common and effective stress task. Research has confirmed that the MAT can produce mental stress in healthy subjects13,14 or migraine subjects15. Subjects were arranged in a quiet space to avoid interference from the outside world, informed of the process, and given a short practice opportunity to eliminate the experimental deviation due to unfamiliarity with operation. The MAT architecture was divided into three stages (Rest, Task, and Recovery) with a total duration of 555 s16, which shows in Fig.9. At the rest stage, subjects were asked to close their eyes and relax in the seat for 1 minute. At the task stage, subjects were asked to watch the questions and answer through a touch screen. At the recovery stage, subjects had to do the same things as the rest stage for 3 minutes. The computer saved the data in the form of comma-separated values after the completion of the MAT.

The MAT architecture. (a) A two-/three-digit addition/subtraction question will be displayed at the center of the screen for 1 second. (b) A countdown circle will be displayed on the screen for 4 seconds to remind the subject the remaining time to think. (c) The screen will be divided into two areas to display an answer separately. Subjects had 1 second to select the correct answer. (d) The screen shows a feedback for the result for 1 second. If the answer was correct, a green circle would be displayed; if the answer was wrong, a red cross would be displayed; if the correct answer was not selected in time, a white question mark would be displayed. Performing (ad) once is a cycle, and the task stage includes 45 cycles.

Recruitment was started only after the approval of the Institutional Review Board (IRB) of the Taipei Veterans General Hospital (No.: 2017-01-010C). All methods in this research were performed in accordance with the relevant guidelines and regulations. The inclusion criteria are subjects from 20 to 60 years old, meeting the diagnostic criteria of the third edition of the International Headache Classification (ICHD-3), and those can fully record the migraine attack pattern and basic personal data. Exclusion criteria are those with any major mental or neurological diseases (including brain damage, brain tumors), smoking habits or alcohol abuse. HC include 13 medical staff of Taipei Veterans General Hospital with an average age of 44.9 8.7 years old. Both CM and MOH are patients in the Neurology Clinic of Taipei Veterans General Hospital. There are 9 and 12 patients with an average age of 34.8 10.9 years old and 45.8 11.2 years old respectively. Informed consent was obtained from all subjects.

The signal of fNIRS can be divided into three aspects: (i) source (intracerebral vs. extracerebral), (ii) stimulus/task relation (evoked vs. non-evoked), and (iii) cause (neuronal vs. systemic)17. In our study, task-evoked neurovascular coupling and spontaneous neurovascular coupling is of primary interest. In order to obtain different types of fNIRS signals for subsequent feature extraction, two different filters were used in parallel in this procedure. The first was the Low-pass filter, a fourth-order Butterworth filter, with a cutoff frequency of 0.1 Hz18, which could filter out systemic noise such as breathing, heartbeat, and Mayer wave, which was 1 Hz, 0.3 Hz, 0.1 Hz respectively. Then the changes of neurovascular coupling signal caused by the entire MAT can be obtained. The second was a band-pass filter with a frequency band of 0.01 Hz0.3 Hz19. The hemodynamics response of the PFC, the signal changes after every stimulation, could be observed.

As the purpose of MAT was to stimulate the PFC, the corresponding two channels, Ch2 and Ch3, were focused on. The collected signals included oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HHb). In addition, two different signals could be obtained by adding or subtracting these two signals, total hemoglobin (HbT) and brain oxygen exchange (COE) respectively. These data were divided into three parts by different stages of MAT (rest, task, recovery).

Feature extraction is a method of sorting out available features from a large range of data. Proper feature extraction will improve the quality of model training. The features used in the experiment, demonstrating in Fig.10, will be introduced one by one below

Low-pass filter

Stage mean difference The average difference of hemoglobin at each stage. In order to observe the average change of fNIRS signal of the subject at different stages.

Transition slope Referring to the article published by Coyle et al.22 in 2004, which is mentioned that the maximum value of light intensity can be detected by fNIRS at about five to eight seconds after stimulation, so we took the maximum value of eight seconds. The slope of the fNIRS signal when the first eight seconds after entering a new stage . Fitting the value of the interval with a linear formula, and the coefficient of the first term is the slope. In order to observe the changes of the fNIRS signal under different stimulation.

Transition slope difference The difference of transition slope. In order to observe the difference in the changes of the fNIRS signal under different stimulation.

Normalization Normalization is a procedure for moving and rescaling data. Feature 1 (sim) 3 were calculated again after this process. The normalized data fall between zero and one, which could compare the differences in the ratio of the characteristics of fNIRS signal among the subjects to the changes in their own signal amplitude.

Band-pass filter

Stage standard deviation The standard deviation of the fNIRS signal at each stage. In order to observe the dispersion level of data.

Stage skewness The skewness of fNIRS signal at each stage. In order to observe the asymmetry of the distribution of the signal value.

Stage kurtosis The kurtosis of the fNIRS signal at each stage, which described the tail length of the distribution of the signal value23. Compared with the value near the average, outliers had a greater impact on the value of kurtosis.

Combining the above-mentioned features, a total of 144 features were obtained. These features were the inputs of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA).

Logistic regression is a model commonly used for classification, but it has some disadvantages. First, logistic regression can only deal with the problem of two classifications, and it will be tricky when it encounters multiple classifications; second, it cannot handle well when faced with a large number of features or variables. The most important thing is that if the amount of data is too small, the results will be unstable due to a lack of basis for optimizing parameters. LDA can offset this disadvantage, especially multi-group performance. LDA has two basic hypotheses. First, the algorithm assumes that each group of data is Gaussian distribution. Second, in order to make the decision boundary have a clear geometric meaning, the covariance matrix of each group of data must be equal. On the other hand, QDA does not have the limitation of covariance matrix. In addition, the credibility of the model was evaluated by leave-one-out cross-validation (LOOCV), which was often used in the small data set and made the performance of the fNIRS diagnostic ability more confident.

Excerpt from:
Migraine classification by machine learning with functional near-infrared spectroscopy during the mental arithmetic task | Scientific Reports -...