According to the data of the World Health Organization, the number of people who die of cardiovascular and cerebrovascular diseases in the world every year is as high as 15 million, ranking first among various causes of death. Cardiovascular and cerebrovascular diseases have become the number one killer with the highest cause of human death, and also the “silent evil” of people’s health!
Symptoms of cardiovascular disease, such as coronary heart disease (including angina pectoris, myocardial infarction) are: chest tightness, restless palpitations, palpitation, shortness of breath; irregular heartbeat; chest pain, pain in the retrosternal or precordial area; belching etc.
In the application of modern medicine, the electrocardiogram is becoming more and more important. It has reliable diagnosis, simple method, real-time ECG monitoring for patients, timely detection of abnormal ECG changes, and plays an important role in preventing and treating cardiovascular diseases. However, the common conventional ECG on the market lacks many drawbacks.
Atrial fibrillation, the most common cardiac arrhythmia, is associated with a higher risk of stroke or heart failure. However, the f-wave signal, which is indicative of atrial fibrillation, is very weak and extremely difficult to detect with conventional portable ECG sensors.
A team led by Dr. Chen Guoliang, associate professor of the Department of Mechanical Engineering of the University of Hong Kong, has developed a wearable electrocardiogram (ECG) sensor that can detect electrophysiological signals of atrial fibrillation for daily use.
Dr. Chen’s team also imitated the memory function of the human brain and successfully stored information in organic transistors, laying a key foundation for machine learning to simulate the function of the human brain.
The above two important scientific research achievements have been published in the international journal “Nature-Communications” respectively.
Among them, a new electrocardiogram sensor, titled “subthermionic, ultra-high-gain organic transistors and circuits” to detect atrial fibrillation, was developed in cooperation with Nanjing University. The signal amplification of this new sensor is excellent, amplifying (gaining) the input signal by more than 10,000 times, and it can detect very weak amplitudes, indicating atrial fibrillation, that is, f-waves with a frequency of 357 beats per minute (BPM).
Preliminary tests show that the sensor can successfully detect abnormal electrophysiological signals in patients with atrial fibrillation, which traditional electrodes cannot.
Based on the single-layer organic field-effect transistor (OFET) previously developed by Dr. Chen, the team mounted flexible and ultra-thin semiconductors on a flexible substrate (material: polyimide). , developed a new type of ECG sensor. The ultra-low subthreshold swing (SS) in the OFET is the key to the high signal detection capability of this ECG sensor.
Dr Chen said: “Subthreshold swing is an important parameter in transistor or inverter operation, indicating how much voltage change is required to turn the device from the ‘off’ state to the ‘on’ state. Our device provides innovative The record low sub-threshold swing ensures that high sensitivity is maintained at very low power consumption (low operating power consumption).”
This new ECG sensor is flexible and foldable, as light as a piece of plastic wrap or close-fitting film, and only needs a small button battery for power supply, making it easy to carry.
“The person wearing the sensor can enjoy the freedom of movement, run around, and even take a shower at will, without connecting any instruments to operate. This time is also an important breakthrough in the application of the new structure OFET we developed.” Chen Dr. said.
The innovative single-layer OFET technology developed by Dr. Chen uses organic substances as semiconductors and has the advantage of flexibility. The research results were published earlier in Advanced Materials and have been applied for a US patent.
Another study by Dr. Chen’s team published in Nature Communications is titled “Imitating associative learning behavior using ion-trapping memory synaptic organic electrochemical transistors”.
The research team successfully imitated the operation of the human brain and implanted “memory”, that is, the collected signals and information, into organic transistors, laying a key foundation for the realization of artificial neural networks that can perform signal recognition and learning like the human brain. .
In a joint experiment conducted by the team with Northwestern University in Illinois, the “ion-retaining agent” polytetrahydrofuran (PTHF) was added to the conducting organic polymer PEDOT:TOS. PTHF can significantly slow the ingress and egress of ions in the PEDOT:TOS channel layer and keep them in the ideal conductance state.
These PTHF ion retaining agents can maintain the ideal conductance state of the device and realize the functions of artificial intelligence “learning” and “memory”, just like the chemical substances between the neurons of the human brain, maintaining the flexible operation of the human brain.
Dr Chen explained: “Our research explains the physical principles of how information is stored in organic electrochemical transistors. By enhancing the ‘learning function’ of the device, it lays an important foundation for the development of next-generation computer artificial intelligence learning. Memory transistors It is the basic architecture for building artificial neural networks, which can perform signal recognition or learning just like the human brain. In the future, we are expected to integrate memory transistors with optical sensors to perform simultaneous image processing and computing like the human brain. ”
“The human-machine interface is a huge research field, and its applications have infinite possibilities and can bring unimaginable benefits to mankind.” Dr. Chen added that he will now focus on the use of advanced materials to develop more complex circuits and reduce running power consumption.