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Exploring Heart Rate Variability with Shell Coding

There is a lot of meaning that can come out of a couple of heartbeats. This article will discuss the simple concept of Heart Rate Variability (HRV). Although the dataset is small, two types of computer tool applications were chosen. The process can be handwritten, but computer tools were chosen for this article.

Scientific information will shed light on the topic of how stress and other dependable factors in health are impacted and affected. The effects of HRV and its role in health ailments are also mentioned in this article.

Several companies have included HRV in their wearable devices or smartwatches. Popular brands such as Google, Apple, and Samsung have incorporated this simple calculation into their proprietary apps. Blending their twist into the mix, HRV has made its way into the realm of digital health for everyday customers. Their wearable products have a built-in integration into the flow of in-app information right in the palm of your hands.

By reading further, monitoring and tracking HRV for its several applicable uses can be determined.

Purpose

HRV is an unknown and hidden conceptual term in health that sometimes gets overlooked when determining health issues. Simple calculations determine HRV. The outcomes that could be derived from this concept are beneficial. Maintaining overall self-managed health can provide some insight into undetectable health information.

What could be learned

Readers may understand how heartbeats can become reliable for other health benefits.

Goal

Obtain more control over monitoring HRV and explain how health holds some dependence on the topic.

Prerequisites

The tools used for this article are found below.

There are plenty of workarounds that can end with the same results. This is merely a baseline and benchmark process which can also include workarounds with other brands. A smartwatch and smartphone with these similar functionalities are recommended if the brand mentioned above is unavailable.

Defining HRV

According to Harvard, HRV can be partially defined with the passage found below.

How is HRV calculated?

Heartbeat rates are tracked within a timeframe. From there, the heartbeat is subtracted from the next. In the case of the last heartbeat rate, the number is subtracted from the previous heartbeat rate. Then, all numbers are calculated and multiplied by themselves. After, convert each Beat Per Minute or BPM value into milliseconds by dividing 60000. Next, find the average of the total sum found in the previous step. Finally, find the square root of the average.

As a side note, applications may round all decimals.

Components of HRV

HRV is mainly based on Beat Per Minute rates for a specific timeframe with some simple math.

This article calculates one HRV rate for one point in time after converting BPM to milliseconds.

The calculation of HRV is based on data collected on September 4, 2022. The results from that particular time are shown below. Data and information produced the interpretations shown below using tools.

Apple Observations

By wearing an Apple Watch and participating in activities, HRV was continuously calculated and generated in the visual shown below.

Graph Observations

The calculation of HRV occurred on three occasions. Based on research, 129ms is a healthy HRV value. Since 129ms is more than 100ms, the value seems to range in the healthy category.

Microsoft Excel Observations

Table Observations

In the table that was produced with Microsoft Excel, decimal numbers appeared often. When comparing the application with the table, the difference seemed minor.

The next step was to fill in missing or general error values. As a result, values of zero occurred. When zeroes are present, those values need to be replaced. Take a look at the Apple Health app to help replace zeroes. Additionally, a trial-and-error method may become necessary.

The trial-and-error method was used as a benchmark that helped determine the value of milliseconds to replace the zeroes. For example, having a difference of one between two heartbeats is 60000. This means that the replacement value must be less than 60000. By using the order of operations in its reverse form, it is possible to find the values and double-check.

In the end, the millisecond value was approximately 37055. The heartbeat difference was 1.272484. Finding those values helped determine the overall HRV value of 129ms.

The steps to find HRV with Python shell are shown below. As a reminder, the following calculation is the first point only.

Import libraries to create a table and perform basic math.

Data was exported from the Health app by taking screenshots and copying-and-pasting text into Excel.

The table shown below was used as the dataset for the coding.

BPM1
69
59
63
66
60
67
70
69
62
55
56
66
56
50
50
50
51
58
50
50
52
57
52
50
54
55
52
49
50
53
50
50
50
48
57
48
51
49

Read the file and assign a new variable to the table.

Change column name.

Calculate heart beat difference.

Convert to a DataFrame format.

Calculate the differences to the power of two.

Convert to a DataFrame format.

Confirm data types.

Convert values to the most appropriate data type.

Fill in the error values with the number found previously with Microsoft Excel.

Fill in the last number accordingly.

Assign a variable for the described function.

Slice the table to only show the mean.

Finally, square root the mean found above.

To show the answer of 128.932045.

The results are shown below.

Overall HRV Observations

Based on the information mentioned prior in the article, the number indicated a healthy HRV rate. This number and all the numbers included in this dataset are recorded for a particular point in time. That is important to know as these numbers can change within the next reading.

It is commonly known that there can be at least one drawback for every benefit. The main drawback is that the device might provide inaccurate data. Consequently, this is just a sample version of monitoring stress and also one of several methods to catch its effects.

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