Heart beat monitoring – what can we learn?

Heart monitoring is becoming increasingly popular with the availability of health monitoring wristbands. Most of these units have built-in microprocessors that analyze the recorded signals (*1) to determine the heart rate – the frequency of the cardiac cycle (beats per minute). But, besides the heart rate, what can we learn from our heart?

The cardiac cycle is controlled through electrical impulses (action potentials), induced by pacemaker cells within the sinoatrial node (SA). Their impulse creates a generalized contraction of the muscle cells, which spreads through the heart. As the electrical impulse travels from the pacemaker cells (located in the top of the hart) from the top of the heart to the bottom, it causes the heart to contract and pump blood through heart cavities, whose combined contraction make up the heartbeat. The rhythm of the pacemaker cells within the SA directly controls the heart rate, which at rest is between 60-100 beats per minute [1].

Determining the cardiac cycle and its dynamics can be done a continuous monitoring of the heart activity, a process can be measured through the analysis of blood flow or by analyzing the electrical activity of the heart muscles*2. The latter is known as electrocardiogram or ECG/EKG (see Figure 1 for an example), and can be done by electrical monitoring sensors like Enobio or Starstim by NE. EKG sensors can be then used to monitor the spread of electrical activity through the heart, a pattern that reflects the activity of the different muscles of the heart, see inlet of Figure 1 for a single cardiac cycle. The relation between the EKG and the underlying electrical activity of a cell is rooted in electromagnetism: a depolarization of the cells towards the positive electrode produces a positive deflection in the EKG. Or in other words, the P wave reflects the depolarization of the upper part of the heart initiated by the SA node; QRS reflects the depolarization of the lower part of the heart and T corresponds to the repolarization that starts at the bottom of the heart. Additionally, in an EKG, different electrodes record electrical activity from different parts of the heart and therefore, reflect activation of different muscles (Figure 1). So, once we can determine the cardiac cycle, what can we learn from them? Below you will find a set of metrics that can be extracted from the cardiac cycle as to analyze the properties of the heartbeat.

 

  • R-R interval: defined as the interval between two heartbeats, which reflects the completion of a cardiac cycle. This metric is actually used to assess the heart rate, which in turn can reflect the action of the CNS (see this post for interesting insights on the R-R interval).
  • Heart rate variability (HRV): reflects the variation in the time interval between heartbeats (RR variability) or how regular is the interval between beats. HRV in healthy subjects is related to emotional arousal –
  • Cardiac efficiency, a concept proposed by Bing et al in 1949 [2], aims to measure how well the energy used by the heart is transformed into actual work. The efficiency of a healthy heart is approximately 20-25% and reductions in this metric are an important prognostic for patients that may undergo cardiac arrest [3]. In wearable technology, this can be translated into a simple metrics of ‘steps per minute/beats per minute’ and although it’s clinical estimation is more complex, it has been propose to serve as a sufficient approximation [3].

 

Figure 1 Channel V2 of an electrocardiogram (ECG or EKG) for different subjects (S1 to S3) and the same subject that is exposed to two different medications (S3 and S3’). At the inset, the nomenclature that specifies different parts of the cardiac cycle (for an animated cardiac cycle image, see here)

Importantly, cardiac cycle and its metrics are regulated by physical exercise through the secretion of acetylcholine (that directly signals the SA node). But is not only exercise that modulates the signaling of acetylcholine. Sleep-wake cycle, thermoregulation or meals affect the heart rate through the SA signaling. Stress, sadness or happiness also alters the heart signaling system. Heartbeat does not only change with internally generated emotions but its variability also changes depending on the mood of the music that you are hearing [5]. In fact, several machine-learning algorithms are being developed as emotion recognition systems, which would allow for the clear differentiation of several emotions based on heart rate metrics (see [4] for instance).

Despite the variety of metrics that can be obtained from heartbeat trackers, its information is barely included in the medical system by health professionals. And the limitation relies in that the vast majorities of commercially available wearables have not been tested in clinical settings and thus, lack approval by the regulatory agencies as medical devices. What would the future bring us? Like mobile phones, wearables enable the user to become autonomous – will we become autonomous towards our health?

 

 

[1] Gordan, Richard, Judith K. Gwathmey, and Lai-Hua Xie. “Autonomic and endocrine control of cardiovascular function.” World journal of cardiology 7.4 (2015): 204.

[2] Bing RJ, Hammond M, Handelsman JC, et al. The measurement of coronary blood flow, oxygen consumption and efficiency of the left ventricle in man. Am Heart J. 1949;38:1–24

[3] Visser, Frans. “Measuring cardiac efficiency: is it clinically useful?.” Am Heart J38 (1949): 1-24.

[4] Yu, Sung-Nien; Chen Shu-Feng; Emotion state identification based on heart rate variability and genetic algorithms. Conf. Proc IEE Eng Med Biol Soc. (2015) Aug:538-41.

[5] Nakahara, Hidehiro, et al. “Emotion‐related Changes in Heart Rate and Its Variability during Performance and Perception of Music.” Annals of the New York Academy of Sciences 1169.1 (2009): 359-362.

*1: Newest devices are optical sensors, generating a signal that is also known as a photoplethysmogram (PPG) – LED lights emit light that it is naturally absorbed by our skin and blood. A photoreceptor reads out the amount of absorbed light – which is larger when a heartbeat pushes all the blood through the sensor.

*2: Other sensors that can be use to monitor heart include pulse oximetry (measure oxygen saturation) or seismocardiogram (records body vibrations induced by heart beat).

 

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