Wearable ECG Monitor

The key difference between ECG measured by Wearable ECG monotor smartwatch measured in hospital

Cart ECG-smartwatch

Medical ECG equipment typically includes 12/18-lead ECG machines in hospitals, dynamic ECG Holter as medical equipment and other equipment. Generally, the patient needs to go to the hospital, and the equipment is installed and worn by a special person. Finally, the doctor completes the analysis and serves as the basis for diagnosis and judgment. If it is a dynamic electrocardiogram, the patient will wear it continuously for a period of time (1 day to 2 weeks), and then return it to the hospital for analysis and diagnosis by a doctor.

The above-mentioned use process is relatively cumbersome, which also directly leads to the in-hospital ECG measurement only when people feel uncomfortable and judged by the doctor to be necessary, and the absolute number of ECG signals measured by each person is very small. However, the strict operation process of in-hospital ECG can ensure the high quality of the collected signals, and at the same time, combined with information such as doctor’s consultation and other inspection results, it has a strong value for the diagnosis and treatment of patients.

From the above, we can see two characteristics of in-hospital ECG: first, the value density is high, and the value comes not only from the large number of leads, but also from the strict operating specifications and the results of the doctor’s analysis; second, the absolute number is small, only the collection time Very isolated moments or small time slices on an axis.

Wearable ECG monotor smartwatch typically have various forms such as watches, patches, and clothing. These devices are generally designed in accordance with To C. Consumers wear them after purchasing and can measure them at any time in their lives. Generally, algorithms are automatically analyzed and reference opinions are given. Considering the convenience of wearing in daily life, such devices generally cannot do 12/18 leads, but do a small number of leads when the wearable conditions are met. In particular, in the form of a watch, generally only the limb I lead is the most common design.

Due to the insufficient number of leads, only the projection of the ECG vector in one or several directions can be seen. Compared with the in-hospital ECG, the collected information must be less. In addition, if the user wears it by himself, due to subjective or objective reasons, the wearing process and usage method cannot meet the standard requirements in the hospital, and the signal quality itself will also be affected.

As the name suggests, the beauty of a wearable ECG device is in the wearable. As long as users can wear them frequently in daily life, they have the opportunity to frequently collect ECG signals. Such measurements are not just one or two time segments in life, but data accumulation over the years. Further, wearable devices are generally not only equipped with one sensor for ECG, but also various sensors such as geographic location, motion, and temperature, plus behavior data such as the user’s own voice, images, and what they are doing during measurement. These data are combined with ECG data. It constitutes a high-dimensional information vector combined with life scenes.

Having said that, the characteristics of wearable ECG devices have also been clarified, which are complementary to in-hospital devices. First, the value density is low, and a single measurement is not enough for direct medical use. There are reasons for signal quality, and there are also reasons why algorithms cannot replace the manual judgment of doctors; second, the absolute number is large and the dimensions are wide. The time dimension is approximately continuous recording, accumulating massive information, and the wide dimension means that physiological changes can be analyzed in combination with scenes.

The above differences can be summarized in the following table

What is the current Wearable ECG monotor smartwatch data suitable for analysis?

As a technician, the author has been thinking about how to analyze medical-grade conclusions from wearable ECG signals for a long time. In this field, many teams have made hard work and good results. However, limited by the key differences mentioned above, it is still quite difficult to balance wearable and medical-grade precision at this stage. Thinking in another direction, since there are essential differences in the acquisition characteristics, is it necessary that the signal quality, the number of leads, and the medical accuracy of the analysis conclusion of the wearable device reach that of the hospital equipment? Maybe not necessary.

Under the current level of wearable ECG technology, high-density ECG signals are collected in the user’s life. Even if these ECG signals are not enough to accurately analyze the lesions of the heart, at least the heartbeat rhythm information can be accurately obtained. In addition, the user’s life scene during each collection: whether he feels uncomfortable or not, whether he is emotionally high or depressed, whether he is at home or in the office, and so on. This is enough to make a meaningful analysis of the user’s daily behavior and health characteristics.

What applications are the analysis results suitable for?

Before this field, the most talked about strong medical applications for the individual wearer, typical scenarios include: recovery after discharge, preliminary screening after feeling unwell in life, etc. These scenes have two characteristics, one is for the individual wearer, and the other is for high requirements on medical precision. For example, in the recovery after discharge, if the changes in the user’s physical condition cannot be detected in time and the follow-up consultation is delayed, there will be serious consequences. This obviously does not match the analysis characteristics discussed in the previous section. If all applications are aimed at individuals and have strict medical precision requirements, then we can only continue to wait for breakthroughs in technology and medical ethics.

Thinking from another angle, there is a massive amount of data generated by continuous measurement, and there is behavior data constructed from life scene information, which has already constructed the massive data foundation of behavior + ECG in daily life scenes. Based on this foundation, what big data technology is best at is the statistical application after full calculation based on massive behavior data, which can be illustrated with a few specific examples.

Risk assessment and management of life insurance

With the scene-based data collected by wearable ECG devices, it is technically possible to calculate the risk of each person’s cardiac diseases in a statistical sense, so as to achieve a more accurate risk assessment. More accurate risk evaluation means more accurate estimation of claims and more reasonable pricing and underwriting strategies, which not only enables users to buy insurance, but also improves the commercial interests of insurance companies.

At the same time, because insurance focuses on statistical applications. As long as the overall risk assessment is more accurate than today’s methods through questionnaires or one-time physical examinations, it is a useful method; it does not pursue the accuracy of each measurement for each person, which avoids the existing possible Wear ECG short board.

Personal health trend monitoring and health management

Through long-term monitoring of the wearer’s personal life scenarios, such as changes in ECG during normal working hours, during overtime, and going home for a rest at night, it is possible to discover the impact of certain living habits on ECG; further, based on the heartbeat calculated by ECG Rhythm can also reflect the activity of sympathetic and parasympathetic nerves, and many studies report that this can be used to estimate personal emotions.

Based on these physiological influences, emotional analysis and life labels, the health advice given is highly personalized and targeted. What users see is no longer the same “regular work and rest, drink more water”, but a combination of their daily Analysis reports and recommendations for lifestyle habits. Such health monitoring and management is in line with the direction of personalized medicine recognized in the medical field. Further, this point can be combined with the previous insurance business. For users who have purchased personal insurance, the stronger the insurance company’s ability to manage the health of this group, the more likely it is to reduce the probability of disease occurrence and reduce costs.

In this health monitoring and management process, the trend is still more important than single-point accuracy, so the shortcomings of existing wearable ECGs can be avoided.

challenges and discussions

The previous article discussed the key differences between wearable ECGs and in-hospital devices, which analysis and applications are suitable for. These analyses and applications are based on data, and in order to collect this data, wearable ECG devices and products must achieve sufficient coverage in the consumer market. But so far, such devices have not been widely used in the market, and the core challenge is that they do not address the motivation of consumers to buy wearable ECG products.

Consumers decide to use a product for at least two considerations:

What are the benefits of wearable ECG ECG?

As a means of medical examination, the habitual thinking of consumers is to use this device to check whether there is a disease. In this dimension, you can get the results confirmed by the doctor when you go to the hospital. Although the wearable device saves the trouble of going to the hospital, the result is “for reference only”, and consumers do not buy it.

Therefore, in product design, emphasis should be placed on health trend management: wearable products are not a substitute for in-hospital medical-grade equipment, but are used to observe user health trends over time and give personalized analysis and prompts.

It should be pointed out that the “health trend” is a medium and long-term concept, which is not consistent with people wanting to know whether they are sick or not. Therefore, the market with complete To C in the early stage is not easy to cold start. Either look for segments with self-health management awareness, or cooperate with units that have health management needs (such as insurance companies).

Cost and ease of use

Since it is not used for diagnosis, the cost of use that consumers are willing to pay is limited. In terms of convenience, the best mode of use is completely passive measurement. After the wearer is authorized to start the ECG measurement, he will not feel the process of ECG measurement at all, and his daily life will not be disturbed, so the use cost can be minimized. Inadvertently complete data accumulation. The current development center electric clothing is more suitable for this scene, but users need to buy such clothing specially.

Another idea is to put the ECG function on a widely existing product, such as watches, bracelets, and mobile phones. Under this idea, the user buys the watch or the mobile phone itself, and the ECG is only an accessory, so the purchase cost may be lower; but In use, passive measurement like ECG clothing cannot be achieved. Usually, users need to place their left and right hands on the device to measure, that is, only users can actively measure, and user habits need to be cultivated.

ECG is the basic signal of human vital signs and contains a lot of physiological information; at the same time, because it is an electrical signal, it is relatively convenient to collect and record. Although there are many challenges, with the development and maturity of the industry, the cost continues to decrease, and the convenience of use continues to improve.

Today’s technology has made it possible for the wearable ECG to become one of the vital sign sensors for a large range of people; further, in the sensor On the basis of technology, today’s ubiquitous mobile Internet infrastructure and applications can also realize ECG signal + life scene label collection, which will form the basis of life behavior data with physiological information. With this data foundation, it is possible for the physiological health big data to find a commercially valuable application direction in the breakthrough of wearable ECG.

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