AI diagnosis of heart sounds differentiated with super StethoScope.

In the aging global society, heart failure and valvular heart diseases, including aortic stenosis, are affecting millions of people and healthcare systems worldwide. Although the number of effective treatment options has increased in recent years, the lack of effective screening methods is provoking continued high mortality and rehospitalization rates. Appropriately, auscultation has been the primary option for screening such patients, however, challenges arise due to the variability in auscultation skills, the objectivity of the clinical method, and the presence of sounds inaudible to the human ear. To address challenges associated with the current approach towards auscultation, the hardware of Super StethoScope was developed. This paper is composed of (1) a background literature review of bioacoustic research regarding heart disease detection, (2) an introduction of our approach to heart sound research and development of Super StethoScope, (3) a discussion of the application of remote auscultation to telemedicine, and (4) results of a market needs survey on traditional and remote auscultation. Heart sounds and murmurs, if collected properly, have been shown to closely represent heart disease characteristics. Correspondingly, the main characteristics of Super StethoScope include: (1) simultaneous collection of electrocardiographic and heart sound for the detection of heart rate variability, (2) optimized signal-to-noise ratio in the audible frequency bands, and (3) acquisition of heart sounds including the inaudible frequency ranges. Due to the ability to visualize the data, the device is able to provide quantitative results without disturbance by sound quality alterations during remote auscultations. An online survey of 3648 doctors confirmed that auscultation is the common examination method used in today's clinical practice and revealed that artificial intelligence-based heart sound analysis systems are expected to be integrated into clinicians' practices. Super StethoScope would open new horizons for heart sound research and telemedicine.

Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review.

Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren't any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts.

Epileptic seizure-related changes in electrocorticogram, cortical temperature, and cerebral hemodynamics obtained via an implantable multimodal multichannel probe during preoperative monitoring: illustrative case.

BACKGROUND: Electrocorticography (EcoG) plays an essential role in the preoperative evaluation of epilepsy, despite its high invasiveness. Brain temperature and cerebral hemodynamics also reflect brain activity. This study examined whether a multimodal multichannel probe that simultaneously records EcoG, cortical temperature, and cerebral hemodynamics can contribute to improving the assessment of epileptic seizures. After preoperative monitoring was performed in a patient with epilepsy, three generalized seizures and two focal seizures were observed. OBSERVATIONS: A short-term power increase in the alternating current spectrogram, high-amplitude slow waves in direct current potential, an increase in cortical temperature, an increase in oxyhemoglobin (HbO2) concentration and total hemoglobin (HbT) concentration, and a decrease in deoxyhemoglobin (HHb) concentration, followed by a decrease in HbO2 and HbT concentrations and an increase in HHb concentration, were observed in generalized seizures. However, no changes in these pathophysiological signals were observed in focal seizures. LESSONS: Seizure-related changes regarding generalized seizures were consistent with the results of previous studies. The results of generalized and focal seizures indicate that epileptic brain activity propagated from the epileptic focus in the right frontal lobe to the measurement area near the motor cortex in generalized seizures but not in focal seizures.

Sympathetic hyperactivity, hypertension, and tachycardia induced by stimulation of the ponto-medullary junction in humans.

OBJECTIVE: The purpose of this study is to investigate changes in autonomic activities and systemic circulation generated by surgical manipulation or electrical stimulation to the human brain stem. METHODS: We constructed a system that simultaneously recorded microsurgical field videos and heart rate variability (HRV) that represent autonomic activities. In 20 brain stem surgeries recorded, HRV features and sites of surgical manipulation were analyzed in 19 hypertensive epochs, defined as the periods with transient increases in the blood pressure. We analyzed the period during electrical stimulation to the ponto-medullary junction, performed for the purpose of monitoring a cranial nerve function. RESULTS: In the hypertensive epoch, HRV analysis showed that sympathetic activity predominated over the parasympathetic activity. The hypertensive epoch was more associated with surgical manipulation of the area in the caudal pons or the rostral medulla oblongata compared to controls. During the period of electrical stimulation, there were significant increases in blood pressures and heart rates, accompanied by sympathetic overdrive. CONCLUSIONS: Our results provide physiological evidence that there is an important autonomic center located adjacent to the ponto-medullary junction. SIGNIFICANCE: A large study would reveal a candidate target of neuromodulation for disorders with autonomic imbalances such as drug-resistant hypertension.

Resting Heart Rate Variability Is Associated With Subsequent Orthostatic Hypotension: Comparison Between Healthy Older People and Patients With Rapid Eye Movement Sleep Behavior Disorder.

Background: Orthostatic hypotension (OH) caused by autonomic dysfunction is a common symptom in older people and patients with idiopathic rapid eye movement sleep behavior disorder (iRBD). The orthostatic challenge test is a standard autonomic function test that measures a decrease of blood pressure during a postural change from supine to standing positions. Although previous studies have reported that changes in heart rate variability (HRV) are associated with autonomic dysfunction, no study has investigated the relationship between HRV before standing and the occurrence of OH in an orthostatic challenge test. This study aims to examine the connection between HRV in the supine position and the occurrence of OH in an orthostatic challenge test. Methods: We measured the electrocardiograms of patients with iRBD and healthy older people during an orthostatic challenge test, in which the supine and standing positions were held for 15 min, respectively. The subjects were divided into three groups: healthy controls (HC), OH-negative iRBD [OH (-) iRBD], and OH-positive iRBD [OH (+) iRBD]. HRV measured in the supine position during the test were calculated by time-domain analysis and Poincaré plots to evaluate the autonomic dysfunction. Results: Forty-two HC, 12 OH (-) iRBD, and nine OH (+) iRBD subjects were included. HRV indices in the OH (-) and the OH (+) iRBD groups were significantly smaller than those in the HC group. The multivariate logistic regression analysis for OH identification for the iRBD groups showed the model whose inputs were the HRV indices, i.e., standard deviation 2 (SD2) and the percentage of adjacent intervals that varied by more than 50 ms (pNN50), had a receiver operating characteristic curve with area under the curve of 0.840, the sensitivity to OH (+) of 1.000, and the specificity to OH (-) of 0.583 (p = 0.023). Conclusions: This study showed the possibility that short-term HRV indices in the supine position would predict subsequent OH in iRBD patients. Our results are of clinical importance in terms of showing the possibility that OH can be predicted using only HRV in the supine position without an orthostatic challenge test, which would improve the efficiency and safety of testing.

Wearable Epileptic Seizure Prediction System with Machine-Learning-Based Anomaly Detection of Heart Rate Variability.

A warning prior to seizure onset can help improve the quality of life for epilepsy patients. The feasibility of a wearable system for predicting epileptic seizures using anomaly detection based on machine learning is evaluated. An original telemeter is developed for continuous measurement of R-R intervals derived from an electrocardiogram. A bespoke smartphone app calculates the indices of heart rate variability in real time from the R-R intervals, and the indices are monitored using multivariate statistical process control by the smartphone app. The proposed system was evaluated on seven epilepsy patients. The accuracy and reliability of the R-R interval measurement, which was examined in comparison with the reference electrocardiogram, showed sufficient performance for heart rate variability analysis. The results obtained using the proposed system were compared with those obtained using the existing video and electroencephalogram assessments; it was noted that the proposed method has a sensitivity of 85.7% in detecting heart rate variability change prior to seizures. The false positive rate of 0.62 times/h was not significantly different from the healthy controls. The prediction performance and practical advantages of portability and real-time operation are demonstrated in this study.

Classification of heart sound signals using a novel deep WaveNet model.

BACKGROUND AND OBJECTIVES: The high mortality rate and increasing prevalence of heart valve diseases globally warrant the need for rapid and accurate diagnosis of such diseases. Phonocardiogram (PCG) signals are used in this study due to the low cost of obtaining the signals. This study classifies five types of heart sounds, namely normal, aortic stenosis, mitral valve prolapse, mitral stenosis, and mitral regurgitation. METHODS: We have proposed a novel in-house developed deep WaveNet model for automated classification of five types of heart sounds. The model is developed using a total of 1000 PCG recordings belonging to five classes with 200 recordings in each class. RESULTS: We have achieved a training accuracy of 97% for the classification of heart sounds into five classes. The highest classification accuracy of 98.20% was achieved for the normal class. The developed model was validated with a 10-fold cross-validation, thus affirming its robustness. CONCLUSION: The study results clearly indicate that the developed model is able to classify five types of heart sounds accurately. The developed system can be used by cardiologists to aid in the detection of heart valve diseases in patients.

Comprehensive electrocardiographic diagnosis based on deep learning.

Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irreversible heart muscle damage, resulting in heart chamber remodeling and eventual congestive heart failure (CHF). Electrocardiography (ECG) signals can be useful to detect established MI, and may also be helpful for early diagnosis of CAD. For the latter especially, the ECG perturbations can be subtle and potentially misclassified during manual interpretation and/or when analyzed by traditional algorithms found in ECG instrumentation. For automated diagnostic systems (ADS), deep learning techniques are favored over conventional machine learning techniques, due to the automatic feature extraction and selection processes involved. This paper highlights various deep learning algorithms exploited for the classification of ECG signals into CAD, MI, and CHF conditions. The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most useful architectures for classification. A 16-layer LSTM model was developed in our study and validated using 10-fold cross-validation. A classification accuracy of 98.5% was achieved. Our proposed model has the potential to be a useful diagnostic tool in hospitals for the classification of abnormal ECG signals.

Emotional Contagion From Humans to Dogs Is Facilitated by Duration of Ownership.

Emotional contagion is a primitive form of empathy that does not need higher psychological functions. Recent studies reported that emotional contagion exists not only between humans but also among various animal species. The dog (Canis familiaris) is a unique animal and the oldest domesticated species. Dogs have coexisted with humans for more than 30,000 years and are woven into human society as partners bonding with humans. Dogs have acquired human-like communication skills and, likely as a result of the domestication process, the ability to read human emotions; therefore, it is feasible that there may be emotional contagion between human and dogs. However, the higher time-resolution of measurement of emotional contagion between them is yet to be conducted. We assessed the emotional reactions of dogs and humans by heart rate variability (HRV), which reflects emotion, under a psychological stress condition on the owners. The correlation coefficients of heart beat (R-R) intervals (RRI), the standard deviations of all RR intervals (SDNN), and the square root of the mean of the sum of the square of differences between adjacent RR intervals (RMSSD) between dogs and owners were positively correlated with the duration of dog ownership. Dogs' sex also influenced the correlation coefficients of the RRI, SDNN, and RMSSD in the control condition; female showed stronger values. These results suggest that emotional contagion from owner to dog can occur especially in females and the time sharing the same environment is the key factor in inducing the efficacy of emotional contagion.

Limitations of Local Brain Cooling on Generalized Motor Seizures from Unknown Foci in Awake Rats.

Local brain cooling of an epileptic focus at 15°C reduces the number of spikes on an electrocorticogram (ECoG), terminates seizures, and maintains neurological functions. In this study, we attempted to suppress generalized motor seizures (GMSs) by cooling a unilateral sensorimotor area. GMSs were induced in rats by intraperitoneal injection of bicuculline methiodide, an antagonist of gamma-aminobutyric acid. While monitoring the ECoG and behavior, the right sensorimotor cortex was cooled for 10 min using an implanted device. The number of spikes recorded from the cooled cortex significantly decreased to 71.2% and 62.5% compared with the control group at temperatures of 15 and 5°C (both P <0.01), respectively. The number of spikes recorded from the contralateral mirror cortex reduced to 61.7% and 62.7% (both P <0.05), respectively. The ECoG power also declined to 85% and 50% in the cooled cortex, and to 94% and 49% in the mirror cortex by the cooling at 15 and 5°C, respectively. The spikes regained in the middle of the cooling period at 15°C and in the late period at 5°C. Seizure-free durations during the 10-min periods of cooling at 15 and 5°C lasted for 4.1 ± 2.2 and 5.9 ± 1.1 min, respectively. Although temperature-dependent seizure alleviation was observed, the effect of local cortical cooling on GMSs was limited compared with the effect of local cooling of the epileptic focus on GSMs.

Implantable Multi-Modality Probe for Subdural Simultaneous Measurement of Electrophysiology, Hemodynamics, and Temperature Distribution.

OBJECTIVE: The purpose of this paper is to demonstrate how the integration of the multi-channel measurement capabilities of near-infrared spectroscopy (NIRS), electrocorticography (ECoG), and negative temperature coefficient thermistor sensors into a single device compact enough for subdural implantation can provide beneficial information on various aspects of brain cortical activity and prove a powerful medical modality for pre-, intra-, and post-operative diagnoses in neurosurgery. METHODS: The development of a flexible multi-modal multi-channel probe for the simultaneous measurement of the NIRS, ECoG, and surficial temperature obtained from the cerebral cortex was carried out. Photoelectric bare chips for NIRS channels, miniature temperature-coefficient thermistors for measuring localized temperature variation, and 3-mm-diameter platinum plates for ECoG recording were assembled on a polyimide-based flexible printed circuit to create six channels for each modality. A conformal coating of Parylene-C was applied on all the channels except the ECoG to make the probe surface biocompatible. RESULTS: As a first-in-human study, the simultaneous measurement capability of the multi-modality probe, with sufficient signal-to-noise ratio and accuracy, to observe pathological neural activities in subjects during surgery and post-operative monitoring, with no complications two weeks since the implantation, was confirmed. CONCLUSION: The feasibility of using a single device to assess the dynamic pathological activity from three different aspects was determined for human patients. SIGNIFICANCE: The simultaneous and accurate multi-channel recording of electrical, hemodynamic, and thermographic cortical activities in a single device small enough for subdural implantation is likely to have major implications in neurosurgery and neuroscience.

Epileptic Seizure Suppression by Focal Brain Cooling With Recirculating Coolant Cooling System: Modeling and Simulation.

A focal brain cooling system for treatment of refractory epilepsy that is implantable and wearable may permit patients with this condition to lead normal daily lives. We have developed such a system for cooling of the epileptic focus by delivery of cold saline to a cooling device that is implanted cranially. The outflow is pumped for circulation and cooled by a Peltier device. Here, we describe the design of the system and evaluate its feasibility by simulation. Mathematical models were constructed based on equations of fluid dynamics and data from a cat model. Computational fluid dynamics simulations gave the following results: 1) a cooling device with a complex channel structure gives a more uniform temperature in the brain; 2) a cooling period of <10 min is required to reach an average temperature of 25.0°Cat 2 mm below the brain surface, which is the target temperature for seizure suppression. This time is short enough for cooling of the brain before seizure onset after seizure prediction by an intracranial electroencephalogram-based algorithm; and 3) battery charging would be required once every several days for most patients. These results suggest that the focal brain cooling system may be clinically applicable.

Heart Rate Variability-Based Driver Drowsiness Detection and Its Validation With EEG.

OBJECTIVE: Driver drowsiness detection is a key technology that can prevent fatal car accidents caused by drowsy driving. The present work proposes a driver drowsiness detection algorithm based on heart rate variability (HRV) analysis and validates the proposed method by comparing with electroencephalography (EEG)-based sleep scoring. METHODS: Changes in sleep condition affect the autonomic nervous system and then HRV, which is defined as an RR interval (RRI) fluctuation on an electrocardiogram trace. Eight HRV features are monitored for detecting changes in HRV by using multivariate statistical process control, which is a well known anomaly detection method. RESULT: The performance of the proposed algorithm was evaluated through an experiment using a driving simulator. In this experiment, RRI data were measured from 34 participants during driving, and their sleep onsets were determined based on the EEG data by a sleep specialist. The validation result of the experimental data with the EEG data showed that drowsiness was detected in 12 out of 13 pre-N1 episodes prior to the sleep onsets, and the false positive rate was 1.7 times per hour. CONCLUSION: The present work also demonstrates the usefulness of the framework of HRV-based anomaly detection that was originally proposed for epileptic seizure prediction. SIGNIFICANCE: The proposed method can contribute to preventing accidents caused by drowsy driving.

Intraoperative Cerebral Measurements Using Implantable Cortical Multimodality Probe.

In this study, a multimodality probe that simultaneously measures electroencephalograms, cerebral hemodynamics, and brain surface temperature was developed. This probe has six channels, and each channel has a platinum electrode for cortical electroencephalogram measurements, light emitting diodes, and photodiodes for hemodynamic measurements using near-infrared spectroscopy (NIRS), and a thermistor for measuring the cerebral surface temperature (BrT). A probe with a width of 8.0 mm and maximum total thickness of 0.7 mm was fabricated using flexible printed circuit board technology for chronic intracranial placement. Brain activity using the prototype probe at the resected site was measured and its function performance was evaluated. Characteristic epileptogenic abnormal electroencephalograms accompanied by polarity reversal between channels occurred at 16 min and 38 s. It was concluded that the brain cells consumed oxygen during the occurrence of abnormal electroencephalograms. At this time, no noticeable change in HbT values could be confirmed.

Is hemifacial spasm affected by changes in the heart rate? A study using heart rate variability analysis.

OBJECTIVE: Hemifacial spasm (HFS) is caused by arterial conflict at the root exit zone of the facial nerve. As the offending artery is pulsatile in nature, this study investigated the association of heart rate fluctuation with HFS. METHODS: Twenty-four preoperative patients underwent simultaneous recordings of facial electromyogram and electrocardiogram overnight. Series of R-wave to R-wave intervals (RRIs) in the electrocardiogram were analyzed across subjects in relation to HFS. The degree of heart rate fluctuation was quantified by analyzing the heart rate variability (HRV). The sleep stage was evaluated during the period of HFS. RESULTS: A 0.1 Hz fluctuation in RRIs by 5% compared to the baseline preceded a few seconds the onset of the HFS, indicating that a significant increase in the heart rate coincided with HFS. HRV analysis demonstrated that fluctuations in the heart rate were significantly enhanced during HFS. Wake or light sleep stages were more often accompanied by HFS, suggesting an association with autonomic activities. CONCLUSION: Our findings suggest that the etiology of HFS is more than just a mechanical compression of the facial nerve and may involve changes in pulsatile frequency in offending arteries. SIGNIFICANCE: We propose the etiology of HFS from a unique standpoint.

Ischemic Stroke Detection by Analyzing Heart Rate Variability in Rat Middle Cerebral Artery Occlusion Model.

Although early reperfusion therapy is effective for acute ischemic stroke, limited therapeutic time-window resulted in only 10% of patients receiving reperfusion therapy. A fast and reliable stroke detection method is desired so that patients can receive early reperfusion therapy. It has been reported that ischemic stroke affects heart rate variability (HRV), which reflects activities of the autonomic nervous function. Thus, ischemic stroke may be detected at an acute stage through monitoring HRV. This paper proposes an HRV-based ischemic stroke detection algorithm by using multivariate statistical process control (MSPC), which is a well-known anomaly detection algorithm. As a feasibility study before collecting a large amount of clinical data from human patients, this paper used the middle cerebral artery occlusion (MCAO) model in rats for collecting HRV data shortly after ischemic stroke onsets. The 11 MCAO-operated rats and 11 sham-operated rats were prepared, and HRV data of three sham-operated rats were used for model construction. The data on the other 19 rats were used for its validation. The experimental result showed that sensitivity and specificity of the proposed algorithm were 82% and 75%, respectively. Thus, the present work shows the possibility of realizing an HRV-based ischemic stroke detection system for human patients.

Differential temperature sensitivity of synaptic and firing processes in a neural mass model of epileptic discharges explains heterogeneous response of experimental epilepsy to focal brain cooling.

Experiments with drug-induced epilepsy in rat brains and epileptic human brain region reveal that focal cooling can suppress epileptic discharges without affecting the brain's normal neurological function. Findings suggest a viable treatment for intractable epilepsy cases via an implantable cooling device. However, precise mechanisms by which cooling suppresses epileptic discharges are still not clearly understood. Cooling experiments in vitro presented evidence of reduction in neurotransmitter release from presynaptic terminals and loss of dendritic spines at post-synaptic terminals offering a possible synaptic mechanism. We show that termination of epileptic discharges is possible by introducing a homogeneous temperature factor in a neural mass model which attenuates the post-synaptic impulse responses of the neuronal populations. This result however may be expected since such attenuation leads to reduced post-synaptic potential and when the effect on inhibitory interneurons is less than on excitatory interneurons, frequency of firing of pyramidal cells is consequently reduced. While this is observed in cooling experiments in vitro, experiments in vivo exhibit persistent discharges during cooling but suppressed in magnitude. This leads us to conjecture that reduction in the frequency of discharges may be compensated through intrinsic excitability mechanisms. Such compensatory mechanism is modelled using a reciprocal temperature factor in the firing response function in the neural mass model. We demonstrate that the complete model can reproduce attenuation of both magnitude and frequency of epileptic discharges during cooling. The compensatory mechanism suggests that cooling lowers the average and the variance of the distribution of threshold potential of firing across the population. Bifurcation study with respect to the temperature parameters of the model reveals how heterogeneous response of epileptic discharges to cooling (termination or suppression only) is exhibited. Possibility of differential temperature effects on post-synaptic potential generation of different populations is also explored.

Epidural focal brain cooling abolishes neocortical seizures in cats and non-human primates.

Focal brain cooling (FBC) is under investigation in preclinical trials of intractable epilepsy (IE), including status epilepticus (SE). This method has been studied in rodents as a possible treatment for epileptic disorders, but more evidence from large animal studies is required. To provide evidence that FBC is a safe and effective therapy for IE, we investigated if FBC using a titanium cooling plate can reduce or terminate focal neocortical seizures without having a significant impact on brain tissue. Two cats and two macaque monkeys were chronically implanted with an epidural FBC device over the somatosensory and motor cortex. Penicillin G was delivered via the intracranial cannula for induction of local seizures. Repetitive FBC was performed using a cooling device implanted for a medium-term period (FBC for 30min at least twice every week; 3 months total) in three of the four animals. The animals exhibited seizures with repetitive epileptiform discharges (EDs) after administration of penicillin G, and these discharges decreased at less than 20°C cooling with no adverse histological effects. The results of this study suggest that epidural FBC is a safe and effective potential treatment for IE and SE.

Epileptic Seizure Prediction Based on Multivariate Statistical Process Control of Heart Rate Variability Features.

OBJECTIVE: The present study proposes a new epileptic seizure prediction method through integrating heart rate variability (HRV) analysis and an anomaly monitoring technique. METHODS: Because excessive neuronal activities in the preictal period of epilepsy affect the autonomic nervous systems and autonomic nervous function affects HRV, it is assumed that a seizure can be predicted through monitoring HRV. In the proposed method, eight HRV features are monitored for predicting seizures by using multivariate statistical process control, which is a well-known anomaly monitoring method. RESULTS: We applied the proposed method to the clinical data collected from 14 patients. In the collected data, 8 patients had a total of 11 awakening preictal episodes and the total length of interictal episodes was about 57 h. The application results of the proposed method demonstrated that seizures in ten out of eleven awakening preictal episodes could be predicted prior to the seizure onset, that is, its sensitivity was 91%, and its false positive rate was about 0.7 times per hour. CONCLUSION: This study proposed a new HRV-based epileptic seizure prediction method, and the possibility of realizing an HRV-based epileptic seizure prediction system was shown. SIGNIFICANCE: The proposed method can be used in daily life, because the heart rate can be measured easily by using a wearable sensor.

Development of NIRS probe based on LED sensing that enables multimodal integration.

We propose LED sensing which provides the miniaturization and symmetrization for NIRS sensor system. In order to make it into practical application, the spectral responses of LEDs were investigated and then formula for calculating changes in hemoglobin concentrations were established. In blood phantom experiment, temporal changes in hemoglobin concentration were observed by CW-NIRS using LED sensing.