Figure 1. Block Diagram of Heart Rate Analysis
This paper deals with the diagnosis and analysis of heart rate using Pulse sensor. The pulse sensor uses the optical Methodology to detect the blood flow through the index finger and offers the advantage of portability. The principle of Pulse sensor is that a certain amount of light energy from infrared is absorbed by blood cells and the remaining amount of light is transmitted to the photodiode. Based on the amount of light transmitted, the flow of blood can be calculated, which is directly related to pulse rate. A PIC (Programmable Interface Controller) microcontroller (PIC16F877A) is programmed to count the pulse and to display the heart rate digitally on an LCD (Liquid Crystal Display). The important feature of this paper is the use of Fast Fourier Transform (FFT), to analyze the heart rate using the pulse signal and to diagnose heart related diseases using MATLAB tool. The performance of this device was compared with manual pulse measurement of heartbeat and ECG (Electrocardiography) signal represented on an oscilloscope. Finally, the heart related disease can be diagnosed using the obtained result after the analysis of pulse signal.
Changes in lifestyle and unhealthy eating habits have resulted in a dramatic increase in incidents of heart and vascular diseases. Furthermore, heart problems are being increasingly diagnosed on younger patients. Heart rate indicates the soundness of heart and helps assessing the condition of cardiovascular system. In clinical environment, heart rate is measured under controlled conditions like blood measurement, heart voice measurement, and Electrocardiogram (ECG).Heart rate means the number of heartbeats per unit of time, usually expressed as beats per minute (bpm). Heart rate measurement is one of the very important parameters of the human cardiovascular system. The heart rate of a healthy adult at rest is around 72 beats per minute (bpm). Babies have a much higher heart rate at around 120 bpm, while older children have heart rates at around 90 bpm. The rate when the pulse returns to normal is an indication of the fitness of the person. Lower than normal heart rates are usually an indication of a condition known as Bradycardia, while higher than normal heart rates are known as tachycardia. There is a great need that patients are able to measure the heart rate in the home environment. The HRM (Heart Rate Measuring) devices employ electrical and optical methods as means of detecting and acquiring heart signals. To make the device portable, a Pulse sensor is used to measure the blood flow, so that the electrical probes can be replaced.
Sensors play an important role in measuring physical parameters such as pressure, temperature, Oxygen saturation level in blood etc., C. Hlenschi, S. Corodeanu, and H. Chiriac proposed that the magneto elastic sensor is used to respond to changes in the ambience that has resulted in many applications involving detection and measurement of pressure, temperature, heart rate [1] and also the sensor system is sensitive to all changes in blood volume including respiration and vasomodulation even though they will also include disturbances which are likely due to variations in the lower pressure venous blood volume[2]. To get the most accurate results with the heart rate sensor, it is best to measure the pulse at the finger tip [3]. Motion artifact is an issue for a pulse oximeter, especially in reflectance mode[4],[5]. So transmission type pulse sensor is used in this paper. The peripheral vascular diagnostics was designed by combining the pulse spectroscopy and advanced signal processing [6] which implies that the improvement of heartbeat detection has been achieved in time frequency representation as proposed by Sebastijan Sprager and Damjan Zazula [7]. Thus, the pulse signal is analyzed using a FFT to calibrate the signals for better accuracy [8]. Primary advantage is the very low driving currents for the LEDs (Light Emitting Diode) [9], [10].
HRM measures the pulse rate through changes of blood flow through an index finger. Each pulse of blood from heart increases the density of blood in the finger pulsatile tissue and causes a decrease in light power received by the photodiode. The photodiode does not pick up a purely AC signal as there are some DC components received from other non-pulsatile tissues and ambient light levels. The varying light levels received are converted into a varying resistance in the photodiode. The varying resistance is converted into a varying voltage by using a resistance network and power source. The device provides an accurate reading of the heart rate using optical technology. The signal (analog) originally was too small to detect, and without amplification proved to be too noisy to extract the heart rate. After amplifying, the signal was fed to the comparator, resulting output in the form of pulses. The signal in the form of pulses is interfaced with microcontroller through its digital port for further processing. The microcontroller is the heart of the circuit as in Figure 1. The proposed work is done using PIC microcontroller. The PIC microcontroller (PIC16F877A) is programmed to acquire the signal using its embedded analog to digital converter (10-bit ADC), and use the readings to compute the heart rate; eventually, the heart rate is digitally displayed on an LCD (2X16 LCD). The data is sent to PC using serial port and thus analysis of heart rate is done using MATLAB tool. Analysis is made by using FFT (Fast Fourier Transform) technique. Fourier transform is a basic and important transform for linear analysis which usually transfers the signal from time domain to frequency domain. Features obtained from FFT are helpful to detect some heart disease or certain cardiac condition.
Figure 1. Block Diagram of Heart Rate Analysis
The Pulse sensor consists of an infrared transmitter LED and an infrared sensor photodiode. The device uses noninvasive infrared light to probe pulse rate in a finger tip. In the transmission type, the emitter and detector are placed on opposite sides of tissue as shown in Figure 2. The LED emits infrared light to the finger of the subject. The photo diode detects this light beam and measures the change of blood volume through the finger tip. When there is a signal coming from the photodiode, that signal needs to be amplified if it is a usable signal. The inverting input of the comparator LM358 and the non inverting input of the comparator is pulled down and is given to sensing terminal.
When the resistance between the positive supply and the non inverting input is high, then resulting is the non-inverting input less than the inverting input making comparator output as logic low at pin1. And when the resistance falls making available a voltage to the non-inverting input higher than inverting input, so that the output of comparator is logic high.
Figure 2. Transmission Type Pulse Sensor
Figure 3. Hardware Prototype of the Heart Rate Measuring Device
The hardware model shown in Figure 3, consists of sensor module and controller module. An 8-bit microcontroller was chosen to process the output signal produced by the amplification stage. The Microchip PIC16F877A was selected due to its additional output and processing power, and also its onboard 10-bit analog to digital converter and in-circuit debugging features. Using this highly integrated microcontroller allowed for a simpler design and trouble shooting debugging process. Due to the use of a microcontroller, to calculate the beats per minute (BPM), it was decided that a liquid crystal display (LCD) module would be the most flexible way of displaying this numerical output. Peak value is also displayed simultaneously based on the blood flow in the finger between LED and Photodiode.
MATLAB's FFT function is an effective tool for computing the discrete Fourier transform of a signal. The typical syntax for computing the FFT of a signal is FFT (x, N). Where, x is the signal, x[n] to transform and N is the number of points in the FFT. N must be at least as large as the number of samples in x[n]. FFT analysis is one of the most efficient methods for analyzing frequency about periodic signals. Although distortions of signals occur due to the user's movement or environmental changes (particularly variations in the light), the noisy signal continues to possess the heartbeat information. Thus, FFT analysis can be employed to obtain the heart rate.
In this paper, FFT Analysis is made on the pulse signal obtained by placing the index finger between the LED and Photodiode. The original pulse signal is filtered and thus the peaks are detected after first pass. Then the signal is again filtered (second pass) and peaks are detected. So based on the detected peaks of the pulse signal heart rate is analyzed and also heart related disease can be diagnosed efficiently.
Figure 4 shows the original pulse signal obtained by keeping the finger between LED and Photodiode taken for the analysis. Initially FFT is applied on the Pulse signal using Equation (1).
Figure 5 represents the FFT filtered pulse, then low frequency components were removed. On the resultant signal inverse FFT is applied given by Equation (2).
The filtered pulse is passed to the filter as shown in Figure 6 to detect peaks. Peaks detected after first pass to the filter is shown in Figure 7.
Figure 4. Original Pulse
Figure 5. FFT Filtered Pulse
Figure 6. Filtered Pulse after First Pass
Figure 7. Detected Peaks after First Pass
The Signal obtained after first pass is again passed to the filter as shown in Figure 8 and after second pass peaks detected are shown in Figure 9 .
Figure10 represents the detected peaks with the original Pulse signal which clearly shows that right peaks are detected.
Figure 8. Filtered pulse after second pass
Figure 9. Peaks after Second Pass
Figure 10. Peaks Detected in the Pulse Signal
In this paper, the design and development of a low cost heart rate measuring device has been presented. The device is ergonomic, portable and cost effective. The HRM device is efficient and easy to use. Also this paper elaborated and discussed the analysis of the Heart rate using pulse sensor. Sensor selection, interfacing and signal processing are mainly discussed. The Pulse sensor consists of an infrared transmitter LED and photodiode as receiver. The device uses noninvasive infrared light to probe pulse rate in a finger tip. The LED emits infrared light to the finger of the subject. The photodiode detects this light beam and measures the change of blood volume through the finger tip and the amplified signal is sent to the PIC microcontroller. Thus, the analog signal is converted to the digital form and displayed as Heart rate. The device has the advantage that it can be used by non-professional people at home to measure the heart rate easily and safely. The device could be further developed into a continuously monitoring device that could be used to detect the heart beat anomalies associated with certain heart conditions. This would be made possible by analyzing the heartbeat signal in the frequency domain.
Since, it is much easier to filter the data in the frequency domain, the raw obtained data is transformed to the frequency domain using fast Fourier transform (FFT) operation. Then peak detection algorithm is applied to the filtered pulse signal to analyze the heart beat signal to detect heart rate. The future work of this proposed system is that the signal analysis is made using some other transform and to compare the results with the ECG signal such that the disease can be diagnosed efficiently.