Acupuncture involves the stimulation of acupoints, which are located at specific sites of the human body, by insertion of fine metal needles, followed by manipulation. Acupuncture has been proven to be an effective treatment in pain relief. Available evidence showed that acupuncture alleviates acute pain in conditions such as postoperative pain, acute back pain, labour pain, primary dysmenorrhea, tension-type headaches and migraines. In addition, acupuncture relieves chronic pain, for example, low back pain (LBP), knee osteoarthritis (KOA), headache, shoulder pain, and neck pain. For other diseases like insomnia, drug addiction and stroke, more high-quality randomized control trials (RCTs) are needed to confirm the efficacy of acupuncture, although there are particular difficulties surrounding adequate blinding and control group designs. Recent biomedical technology unveils the mechanisms of acupuncture. Studies have found that adenosine triphosphate (ATP) and transient receptor potential vanilloid (TRPV) channels are involved in the stimulation of acupuncture at the acupoint area. In the central nervous system (CNS), neurotransmissions including opioids, serotonin, norepinephrine, orexin and endocannabinoid are modulated by acupuncture to induce analgesia. Moreover, acupuncture reduces cyclooxygenase-2 (COX-2) and prostaglandin E2 (PGE2) levels on the peripheral level by acting on the hypothalamic-pituitary-adrenal (HPA) axis, mediating peripheral opioid release. Acupuncture helps to treat insomnia by inhibiting sympathetic activity and down-regulating the HPA axis. Additionally, acupuncture reduces the effects of positive and negative reinforcements by modulating dopamine release in the nucleus accumbens. Recently, i-needles have been developed to allow for the analysis of metagenomics, meta-transcriptomics, and host-microbiome relationships following acupuncture, while skin implantable microsensors or needle-shaped microsensors are feasible for monitoring real-time microenvironmental changes in acupoints and even target organs. These studies may further accelerate the understanding of acupuncture's action mechanism.

Understandings of acupuncture application and mechanisms.

Acupuncture involves the stimulation of acupoints, which are located at specific sites of the human body, by insertion of fine metal needles, followed by manipulation. Acupuncture has been proven to be an effective treatment in pain relief. Available evidence showed that acupuncture alleviates acute pain in conditions such as postoperative pain, acute back pain, labour pain, primary dysmenorrhea, tension-type headaches and migraines. In addition, acupuncture relieves chronic pain, for example, low back pain (LBP), knee osteoarthritis (KOA), headache, shoulder pain, and neck pain. For other diseases like insomnia, drug addiction and stroke, more high-quality randomized control trials (RCTs) are needed to confirm the efficacy of acupuncture, although there are particular difficulties surrounding adequate blinding and control group designs. Recent biomedical technology unveils the mechanisms of acupuncture. Studies have found that adenosine triphosphate (ATP) and transient receptor potential vanilloid (TRPV) channels are involved in the stimulation of acupuncture at the acupoint area. In the central nervous system (CNS), neurotransmissions including opioids, serotonin, norepinephrine, orexin and endocannabinoid are modulated by acupuncture to induce analgesia. Moreover, acupuncture reduces cyclooxygenase-2 (COX-2) and prostaglandin E2 (PGE2) levels on the peripheral level by acting on the hypothalamic-pituitary-adrenal (HPA) axis, mediating peripheral opioid release. Acupuncture helps to treat insomnia by inhibiting sympathetic activity and down-regulating the HPA axis. Additionally, acupuncture reduces the effects of positive and negative reinforcements by modulating dopamine release in the nucleus accumbens. Recently, i-needles have been developed to allow for the analysis of metagenomics, meta-transcriptomics, and host-microbiome relationships following acupuncture, while skin implantable microsensors or needle-shaped microsensors are feasible for monitoring real-time microenvironmental changes in acupoints and even target organs. These studies may further accelerate the understanding of acupuncture's action mechanism. 

A novel angle extremum maximum method for recognition of pulse wave feature points.

A machine learning strategy for fast prediction of cardiac function based on peripheral pulse wave

Can statistical and entropy-based features extracted from ECG signals efficiently differentiate the cannabis-consuming women population from the non-consumer?

In recent years, Traditional Chinese Medicine (TCM) has attracted more and more attention due to its good therapeutic effect, low cost, and convenience. This research is also a part of the goal of the modernization of TCM. Based on the meridian electric potential acquisition system independently developed by our project team, in this paper, we designed the human body's meridian electric potential acquisition scheme. We use principal component analysis (PCA) to prove that the meridional potential signal is derived from the ECG signal. Then, Inception ResNet V2 was used to classify acupoints and nonacupoints. Finally, the classification accuracy rate reached 86.59045265, and the F1 score = 0.72161642. This shows that acupoints and nonacupoints can be distinguished by their surface potential.

/pdf/study-on-potential-of-meridian-acupoints-of-traditional-uyqx7y74cl.pdf

Study on Potential of Meridian Acupoints of Traditional Chinese Medicine

Pulse wave contains human physiological and pathological information. Different people will exhibit different characteristics, and hence determining the characteristic points of the pulse wave of human physiological health makes sense. It is common that we extract the characteristic value of pulse wave signal with the method based on wavelet transform on a small scale, and then determine the locations of the characteristic points by modulus maxima and modulus minima. Before determining characteristic value by detecting modulus maxima and modulus minima, we need to determine every period of the pulse wave. This paper presents a new kind of adaptive threshold determination method which is more effective. It can accurately determine every period of the pulse wave, and then extract characteristic values by modulus maxima and modulus minima in every period of the pulse wave. The method presented in this paper promotes the research utilizing pulse wave on health life.

/pdf/a-more-effective-method-of-extracting-the-characteristic-3vcq5022n3.pdf

A More Effective Method of Extracting the Characteristic Value of Pulse Wave Signal Based on Wavelet Transform

In this paper, an FPGA-based convolutional neural network coprocessor is proposed. The coprocessor has a 1D convolutional computation unit PE in row stationary (RS) streaming mode and a 3D convolutional computation unit PE chain in pulsating array structure. The coprocessor can flexibly control the number of PE array openings according to the number of output channels of the convolutional layer. In this paper, we design a storage system with multilevel cache, and the global cache uses multiple broadcasts to distribute data to local caches and propose an image segmentation method that is compatible with the hardware architecture. The proposed coprocessor implements the convolutional and pooling layers of the VGG16 neural network model, in which the activation value, weight value, and bias value are quantized using 16-bit fixed-point quantization, with a peak computational performance of 316.0 GOP/s and an average computational performance of 62.54 GOP/s at a clock frequency of 200 MHz and a power consumption of about 9.25 W.

/pdf/an-fpga-based-convolutional-neural-network-coprocessor-4lk49dqhe5.pdf

An FPGA-Based Convolutional Neural Network Coprocessor

This study introduces a method to classify single-lead ECG signals by extracting features through traditional methods and deep neural network methods. At first step, the statistical type features of the ECG signals are exacted by traditional methods, including time domain features, frequency domain features, and medical domain features. And then, deep neural networks are used to extract the deeper features of the ECG signal. The database of ECG signals is from Cinc 17, which have 8528 samples of short-time ECG signal. The huge amount of data makes the classification and identification more accurate by atrial fibrillation, normal sinus rhythm, noise, and indiscernible. Compare the base model built by the classified data and the data collected by the ECG device of CareON to enable daily early screening and a remote alert function with WeChat app. This method can extend the prevention, detection, and diagnosis of heart disease to the family, company, and other out-of-hospital scenarios, thus enabling faster treatment of heart patients and saving medical resources.

/pdf/an-early-warning-of-atrial-fibrillation-based-on-short-time-q9l85vnv.pdf

An Early Warning of Atrial Fibrillation Based on Short-Time ECG Signals

This paper proposes an architecture based on residual network and bi-directional LSTM for analyzing short-term ECG signals. We discriminate basic cardiac disease by using statistical time domain features, frequency domain features, nonlinear domain features and deep features of ECG signals through traditional methods and deep neural networks. Also, this paper integrates the residual network and bi-directional LSTM to focus on the sequence information contained in the ECG signals better. Cinc Challenge 17 database is used as model evaluation. Our method achieves average F1 score of 0.8682 and accuracy of ECG multi-classification 91%, which proves that this method has a certain degree of auxiliary diagnosis of ECG signals.

Tianxia Zhao

Papers

Study on Potential of Meridian Acupoints of Traditional Chinese Medicine

A More Effective Method of Extracting the Characteristic Value of Pulse Wave Signal Based on Wavelet Transform

An FPGA-Based Convolutional Neural Network Coprocessor

An Early Warning of Atrial Fibrillation Based on Short-Time ECG Signals

A Short-term ECG Signal Classification Method Based on Residual Network and Bi-directional LSTM