Patent Description:
The disclosure herein generally relates to monitoring cardiopulmonary signals, and, more particularly, to selecting an optimum channel in a twin radar for cardiopulmonary signal monitoring.

Doppler radars have been used for detecting respiration rate of a human being without compromising safety and privacy of the person considering they are positioned at a distance from the subject being monitored. Also, temperature, humidity, clothing worn by the subject, or such factors do not affect the monitoring of the subject. The small angle approximation techniques of phase demodulation of Continuous Wave (CW) Doppler radar for detecting very minute respiratory-chest wall movements suffer from inherent optimum-null point problem. This in turn hinders accurate respiratory rate or breathing rate (BR) or heart rate (HR) computations. Cardiopulmonary rate is a precursor for the onset of many ailments ranging from anxiety to lung and heart ailments. It is also a biomarker for cognitive activities, physical as well as mental workload and stress. Hence it is pertinent that cardiopulmonary rate be accurately computed.

<NPL>" refers to two key problems in applying Doppler radar to a diagnosis system for sleep apnea-hypopnea syndrome. The first is noise associated with body movement and the second is the body position in bed and the change of the sleeping posture. A new automatic gain control and a real time radar-output channel selection method is proposed which is based on a spectrum shape analysis. There are three types of sleep apnea: central sleep apnea, obstructive sleep apnea and mixed sleep apnea. Attention to the obstructive sleep apnea and attempted to detect the disorder of corrugated shape compared with usual breathing or the paradoxical movement of the reversed phase with chest and abdominal radar signals. A prototype of the system was set up at a sleep disorder center in a hospital and field tests were carried out with eight subjects. Despite the subjects engaging in frequent body movements while sleeping, the system was quite effective in the diagnosis of sleep apnea-hypopnea syndrome (r=<NUM>).

Embodiments and examples of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems.

In an aspect, there is provided a processor implemented method for optimum channel selection in a twin radar characterized by a first channel and a second channel, the method comprising the steps of: receiving a time domain signal reflected from a subject being monitored, by each of the first channel and the second channel respectively; filtering, by a low pass filter, the time domain signal received by each of the first channel and the second channel respectively, to obtain a filtered time domain signal having frequencies less than a predetermined frequency corresponding to restful breathing associated with the subject being monitored; simultaneously computing, via one or more hardware processors, a peak to peak value (APP<NUM>) of an autocorrelation function (ACF) of the filtered time domain signal associated with the first channel and the second channel respectively, for a predefined time window; and an average value (VPP<NUM>) of peak to peak amplitude of the filtered time domain signal associated with the first channel and the second channel respectively, for the predefined time window; and selecting, via the one or more hardware processors, either the first channel or the second channel as the optimum channel based on the computed peak to peak value of the ACF and the average value of the peak to peak amplitude of the filtered time domain signal.

In another aspect, there is provided a system for optimum channel selection in a twin radar characterized by a first channel and a second channel, the system comprising: one or more data storage devices operatively coupled to one or more hardware processors and configured to store instructions configured for execution via the one or more hardware processors to: receive a time domain signal reflected from a subject being monitored, by each of the first channel and the second channel respectively; filter, by a low pass filter, the time domain signal received by each of the first channel and the second channel respectively, to obtain a filtered time domain signal having frequencies less than a predetermined frequency corresponding to restful breathing associated with the subject being monitored; simultaneously compute: a peak to peak value (APP<NUM>) of an autocorrelation function (ACF) of the filtered time domain signal associated with the first channel and the second channel respectively, for a predefined time window; and an average value (VPP<NUM>) of peak to peak amplitude of the filtered time domain signal associated with the first channel and the second channel respectively, for the predefined time window; and select either the first channel or the second channel as the optimum channel based on the computed peak to peak value of the ACF and the average value of the peak to peak amplitude of the filtered time domain signal.

In yet another aspect, there is provided a computer program product comprising a non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: receive a time domain signal reflected from a subject being monitored, by each of the first channel and the second channel respectively; filter, by a low pass filter, the time domain signal received by each of the first channel and the second channel respectively, to obtain a filtered time domain signal having frequencies less than a predetermined frequency corresponding to restful breathing associated with the subject being monitored; simultaneously compute: a peak to peak value (APP<NUM>) of an autocorrelation function (ACF) of the filtered time domain signal associated with the first channel and the second channel respectively, for a predefined time window; and an average value (VPP<NUM>) of peak to peak amplitude of the filtered time domain signal associated with the first channel and the second channel respectively, for the predefined time window; and select either the first channel or the second channel as the optimum channel based on the computed peak to peak value of the ACF and the average value of the peak to peak amplitude of the filtered time domain signal.

In accordance with the present disclosure, the one or more processors are further configured to select either the first channel or the second channel as the optimum channel by: comparing the peak to peak value (APP<NUM>) associated with the first channel and the second channel, respectively and selecting a channel associated with a higher peak to peak value of the ACF as a first potential optimum channel; comparing the average value (VPP<NUM>) associated with the first channel and the second channel, respectively and selecting a channel associated with a higher average value of the peak to peak amplitude of the voltage signal as a second potential optimum channel; and performing one of: if the first potential optimum channel and the second potential optimum channel are identical, selecting an associated channel as the optimum channel; or computing variance of peak amplitudes associated with the first potential optimum channel and the second potential optimum channel and selecting a channel associated with a lesser variance of peak amplitudes as the optimum channel.

In accordance with the present disclosure, the one or more processors are further configured to perform spectrum analyses on the selected optimum channel to obtain breathing rate of the subject being monitored by: transforming the filtered time domain signal associated with the selected optimum channel to a frequency domain signal using a Fast Fourier transform (FFT) method; and obtaining the breathing rate of the subject being monitored in breaths per minute, based on a frequency associated with a highest peak of the frequency domain signal.

In accordance with the present disclosure, the one or more processors are further configured to perform spectrum analyses on the selected optimum channel to obtain heart rate of the subject being monitored by: filtering the time domain signal received by the selected optimum channel, by a band pass filter to obtain a filtered time domain signal having frequencies in a predetermined range corresponding to restful heart rate range associated with the subject being monitored; transforming the filtered time domain signal associated with the selected optimum channel to a frequency domain signal using a Fast Fourier transform (FFT) method; and obtaining the heart rate of the subject being monitored in beats per minute, based on a frequency associated with a highest peak of the frequency domain signal.

Exemplary embodiments and examples are described with reference to the accompanying drawings. It is intended that the following detailed description be considered as exemplary only, with the true scope being indicated by the following claims.

Considering the significance of cardiopulmonary signal monitoring to timely detect lung or heart ailments, Doppler radars have been gaining traction as a means to measure breathing rate. Radars are unaffected by ambient light conditions and there are no privacy issues associated as seen in vision-based approaches. Since radar-based approaches are unobtrusive, they can be implemented for continuous cardiopulmonary signal monitoring of infants, elderly people, patients and even for animal care.

Continuous Wave (CW) radars provide hardware simplicity and works with simple signal processing techniques. However, CW radars measure velocity of the target only. In the present disclosure, phase difference between continuously transmitted and received signal of a CW radar is demodulated to obtain the rate at which the chest wall moves due to respiration or heart rate. Phase of the baseband signal of a CW radar is a sinusoidal function of the summation of the distance from the subject and its chest wall movement. In the context of the present disclosure, the expression 'subject' refers to a living being such as a human or an animal. The amplitude of chest wall movement, corresponding to the displacement due to respiration is small as compared to the carrier wavelength of a radar. Thus, small angle approximation technique can be used for phase demodulation. Optimum detection occurs when the phase shift due to distance from the subject θ<NUM> is an odd multiple of <MAT>. The baseband output becomes a linear function of time varying chest wall movement. Conversely for θ<NUM> being an even multiple of <MAT>, null detection occurs. These null and optimum points alternate by a distance of one-eighth ties the transmitting wavelength λ of the radar.

State-of-the-art uses extra hardware or an IQ (In-phase and Quadrature) channel radar along with signal processing techniques. The techniques are plagued by either IQ imbalances or need continuous calibration. A simple and cost-effective solution was provided by the Applicant in <CIT> titled `Real Time Unobtrusive Monitoring Of Physiological Signals', wherein a dual radar setup referred as indented radar was disclosed. The indented radar employs optimum spatial placement of two single channel CW radars to compensate for the optimum-null point problem and to have the two single channel CW radars behaving as pseudo I and Q radars respectively. The system disclosed consists of placing two single channel radars, such that the optimum point of one radar is placed at the null point of the other radar, in order to get high fidelity data at any given distance. Depending on the position of the subject with respect to the dual radar setup, any one of the channels maybe null or optimum. However, for a non-rigid subject like a human sitting in front of the indented radar, constant and automated null and optimum channel selection is a requisite for accurately monitoring cardiopulmonary signals. This problem is augmented by the fact that a physiological signal such as breathing rate is of very low frequency, low bandwidth and low amplitude. Due to the specific characteristic of the signal under consideration, a lot of frequency domain analysis have limitations, when applied to such a signal.

The present disclosure has addressed this issue by providing a data driven approach of automated selection of the optimum channel between the two channels of a twin radar or a dual channel radar. Although the technical problem was realized by the Applicant after providing the disclosure of Application no. <CIT>, it may be understood by a person skilled in the art, that the present disclosure may be applicable to a twin radar, in general, and is not limited to use of the indented radar of Application no. In the context of the present disclosure, the expression 'radar' refers to a twin radar characterized by a first channel and a second channel. Particularly, the first channel and the second channel are CW radars. A time domain approach which is time window adaptive, computationally inexpensive and demands no prior calibration either at the subject level or at the system level is provided.

<FIG> illustrates an exemplary block diagram of a system for optimum channel selection in a twin radar for cardiopulmonary signal monitoring, in accordance with some embodiments of the present disclosure. In an embodiment, the system <NUM> is characterized by the state representation model and includes one or more processors <NUM>, communication interface device(s) or input/output (I/O) interface(s) <NUM>, and one or more data storage devices or memory <NUM> operatively coupled to the one or more processors <NUM>. The one or more processors <NUM> that are hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, graphics controllers, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) are configured to fetch and execute computer-readable instructions stored in the memory. In the context of the present disclosure, the expressions 'processors' and `hardware processors' may be used interchangeably. In an embodiment, the system <NUM> can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like.

I/O interface(s) <NUM> can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface(s) can include one or more ports for connecting a number of devices to one another or to another server.

The memory <NUM> may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, one or more modules (not shown) of the system <NUM> can be stored in the memory <NUM>.

In an embodiment, a twin radar <NUM> is characterized by the first channel and the second channel, each being configured to transmit and receive a time domain signal reflected from the subject being monitored. In an embodiment, a filter <NUM> may be at least one of a low pass filter or a band pass filter depending on whether the breathing rate or the heart rate of the subject is being monitored as described later herein below. Furthermore, in an embodiment, the filter <NUM> may be part of the system <NUM>, wherein the one or more hardware processors <NUM> may be configured to serve as the filter <NUM>.

<FIG> illustrates a high-level flow chart of a method for optimum channel selection in the twin radar for cardiopulmonary signal monitoring. For each pre-defined time window, two methods (method <NUM> and method <NUM>) are employed simultaneously to select an optimum channel amongst the first channel and the second channel of the twin radar. Once the optimum channel is selected, breathing rate or heart rate of the subject being monitored may be computed by performing a spectrum analysis.

<FIG> illustrate an exemplary flow diagram of a computer implemented method <NUM> for optimum channel selection in the twin radar for cardiopulmonary signal monitoring, in accordance with some embodiments of the present disclosure. In an embodiment, the system <NUM> includes one or more data storage devices or memory <NUM> operatively coupled to the one or more processors <NUM> and is configured to store instructions configured for execution of steps of the method <NUM> by the one or more processors <NUM>. The steps of the method <NUM> will now be explained in detail with reference to the high-level flow chart of <FIG> and the components of the system <NUM> of <FIG>. Although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously.

In accordance with an embodiment of the present disclosure, the one or more processors <NUM> are configured to receive, at step <NUM>, a time domain signal reflected from the subject being monitored, by each of the first channel and the second channel respectively. Further at step <NUM>, the time domain signal received by each of the first channel and the second channel respectively, are filtered by the filter <NUM>. In an embodiment, the filter utilized at step <NUM> is a low pass filter that filters the time domain signal to obtain a filtered time domain signal having frequencies less than a predetermined frequency corresponding to restful breathing associated with the subject being monitored. In an embodiment, if the subject is a human being, the restful breathing and accordingly, the predetermined frequency is <NUM>.

In accordance with an embodiment of the present disclosure, the one or more processors <NUM> are configured to simultaneously compute, at step <NUM>, the method <NUM> and the method <NUM> referred in <FIG> before. Breathing signal of a healthy subject at rest is highly periodic. The optimum channel reproduces the fundamental frequency, whereas null channel picks a lot of harmonics and noise. Thus, at any time, the signal of optimum channel will be more autocorrelated than the null channel. This understanding forms the basis of method <NUM> for distinguishing between the null and optimum channel. The method <NUM> accordingly involves computing, at step 406A, a peak to peak value (APP<NUM>) of an autocorrelation function (ACF) of the filtered time domain signal associated with the first channel and the second channel respectively, for the predefined time window. For a waveform g(t), existing in an interval T<NUM> to T<NUM>, the autocorrelation function Øgg(τ), for a lag τ, is given by equation (<NUM>) below.

Generally, a breath cycle is <NUM> to <NUM> seconds. If the time window is less than <NUM> seconds, it is difficult to capture <NUM> to <NUM> breaths in a cycle. In accordance with the present disclosure, based on domain knowledge, the predefined time window is at least <NUM> seconds or multiples of <NUM> (<NUM> seconds, <NUM> seconds, etc.).

Another differentiating property of a time series signal of a null channel is its low peak to peak value of amplitude (Vpk-pk) as compared to that of an optimum channel. The method <NUM> involves computing, at step 406B, an average value (VPP<NUM>) of peak to peak amplitude of the filtered time domain signal associated with the first channel and the second channel respectively, for the predefined time window.

In accordance with an embodiment of the present disclosure, the one or more processors <NUM> are configured to select, at step <NUM>, either the first channel or the second channel as the optimum channel based on the computed peak to peak value of the ACF and the average value of the peak to peak amplitude of the filtered time domain signal.

In accordance with an embodiment of the present disclosure, the step of selecting either the first channel or the second channel as the optimum channel comprises comparing the the peak to peak value (APP<NUM>) associated with the first channel and the second channel, respectively. The channel associated with a higher peak to peak value of the ACF is selected as a first potential optimum channel. Then, the average value (VPP<NUM>) associated with the first channel and the second channel, respectively are compared. The channel associated with a higher average value of the peak to peak amplitude of the voltage signal is selected as a second potential optimum channel. If the first potential optimum channel and the second potential optimum channel are identical, there is no conflict, and an associated channel is selected as the optimum channel. Method <NUM> may give a false positive when sudden noisy peaks corrupt the average value (VPP<NUM>). Accordingly, if there is a conflict and the first optimum channel identified by the method <NUM> and the second optimum channel identified by the method <NUM> are different, then variance of peak amplitudes associated with the first potential optimum channel and the second potential optimum channel are computed. For periodic signals, variance of peak amplitudes is less due to the absence of sudden noisy peaks. The channel associated with a lesser variance of peak amplitudes is then selected as the optimum channel.

Once the optimum channel in the twin radar is selected, either the breathing rate of the heart rate may be computed by performing spectrum analyses on the selected optimum channel. In accordance with an embodiment of the present disclosure, the one or more processors <NUM> are configured to obtain breathing rate of the subject being monitored, at step <NUM>, by transforming the filtered time domain signal associated with the selected optimum channel to a frequency domain signal suing a Fast Fourier Transform (FFT) method. The breathing rate of the subject being monitored is obtained in breaths per minute based on a frequency associated with a highest peak of the frequency domain signal. In the scenario where the subject is a human being, the breathing rate is obtained as a product of the frequency and <NUM> (seconds).

In accordance with an embodiment of the present disclosure, the one or more processors <NUM> are configured to obtain heart rate of the subject being monitored, at step <NUM>, by filtering the time domain signal received by the selected optimum channel, by the filter <NUM>. In this scenario, the filter is a band pass filter configured to filter the time domain signal to obtain a filtered time domain signal having frequencies in a predetermined range corresponding to restful heart rate range associated with the subject being monitored. In the scenario where the subject is a human, the restful heart rate and accordingly the predetermined range is <NUM> to <NUM>. The filtered time domain signal associated with the selected optimum channel is transformed to a frequency domain using a Fast Fourier Transform (FFT) method. The heart rate of the subject being monitored is obtained in beats per minute, based on a frequency associated with the highest peak of the frequency domain signal. In the scenario where the subject is a human being, the heart rate is obtained as a product of the frequency and <NUM> (seconds).

<NUM> subjects were requested to sit on a chair at a distance of about <NUM> from an indented radar system (twin radar disclosed in Application no. The twin radar has two single channel <NUM> CW radars. Data was collected at a sampling rate of <NUM> for <NUM> seconds. The subjects were asked to completely relax for <NUM> minutes and then data was acquired via NI DAQ (National Instruments Data Acquisition) and Labview (National Instruments product). The subjects wore normal garments (typically two layers of clothing) and were told to completely relax with eyes closed. To minimize motion artifacts, subjects were requested to avoid sudden movements. For ground truth, as well as surrounding clutter information, a video recording of the subjects was done while experimentation.

Signal pre-conditioning for each channel involved DC component removal followed by low pass filtering. Breathing rate for a normal healthy person ranges from <NUM> to <NUM> breaths per minute. So, the time domain signal was passed through a second order low pass filter of cut off frequency <NUM>. All signal processing was done in Matlab 2018a. The method <NUM> was implemented on the data with different window lengths viz. , <NUM> seconds, <NUM> seconds and <NUM> seconds. This was done to assess the best possible choice of computing the breathing rate. Hence in total, there were <NUM>, <NUM> and <NUM> window segments of data respectively.

<FIG> illustrate a time domain signal, each received by a first channel (represented as channel <NUM> in <FIG>,<FIG>,<FIG>,<FIG>) and a second channel (represented as channel <NUM> in <FIG>,<FIG>,<FIG>,<FIG>) respectively of the twin radar. <FIG> illustrates autocorrelation of the time domain signals of <FIG> respectively, for a <NUM> second window, in accordance with some embodiments of the present disclosure. It may be noted that the second channel has a higher periodicity and autocorrelation function clearly shows it. Accordingly, the second channel was chosen as the optimum channel according to the invention.

<FIG> illustrate a time domain signal, each received by a first channel and a second channel respectively of the twin radar. <FIG> illustrates autocorrelation of the time domain signals of <FIG> respectively, for a <NUM> second window, in accordance with some embodiments of the present disclosure. The first channel and the second channel are between the optimum and the null position and thus the autocorrelation of the second channel is equal to the autocorrelation of the first channel. Accordingly, selecting either of the channels serves the purpose of monitoring cardiopulmonary signals.

<FIG> illustrate a time domain signal, each received by a first channel and a second channel respectively of the twin radar. <FIG> illustrates autocorrelation of the time domain signals of <FIG> respectively, for a <NUM> second window, in accordance with some embodiments of the present disclosure. A conflict scenario is depicted in <FIG>. Channel <NUM> has a periodic signal or the autocorrelation of the second channel is greater but the peak to peak amplitude of channel <NUM> is higher. Method <NUM> identifies channel <NUM> as a first potential optimum channel while method <NUM> identifies channel <NUM> as a second potential optimum channel. In accordance with the present disclosure, channel <NUM> with lesser variance of peak amplitudes is selected as the optimum channel.

It may be understood by persons skilled in the art that variance alone may not be sufficient to decide whether a channel is optimum or not. For instance, based on radar physics, a channel with higher peak to peak amplitude is considered as the optimum channel. There may be a scenario wherein the autocorrelation for that channel is also higher. It is quite possible that it may have a higher variance and hence may not be considered an optimum channel if the variance is checked at the outset for selecting the optimum channel.

<FIG> illustrates a frequency spectrum of the selected optimum channel, in accordance with some embodiments of the present disclosure. <FIG> illustrates the time domain signal associated with the selected optimum channel, in accordance with some embodiments of the present disclosure. From <FIG>, the highest peak for the window is at a frequency <NUM>. This calculates to <NUM> breaths per <NUM> second window.

<FIG> illustrates Mean Absolute Error (MAE) and Standard Deviation (SD) for the selected optimum channel (ChSel), in accordance with some embodiments of the present disclosure. It may be seen that, the MAE and its SD for the selected optimum channel is the least as compared to channel <NUM> (Ch1) and channel <NUM> (Ch2) independently for every time window. For <NUM> second windows, MAE(+SD) is <NUM>(±<NUM>), <NUM>(±<NUM>), and <NUM>(±<NUM>); for <NUM> second windows, it <NUM>(±<NUM>), <NUM>(±<NUM>) and <NUM>(±<NUM>) and for <NUM> second windows, it is <NUM>(+<NUM>), <NUM>(+<NUM>) and <NUM>(+<NUM>) for the method of the present disclosure, channel <NUM> and channel <NUM> respectively.

In accordance with the present disclosure, as seen from the experimental evaluation also, the steps of the method <NUM> are independent of the predefined time window, thereby making the method <NUM> window adaptive. Again, method <NUM> of the present disclosure is a signal property based method while method <NUM> of the present disclosure is a radar physics based method, making the use of the combination non-obvious to a person skilled in the art. Again, the additional elements of receiving the time domain signal reflected from the subject being monitored and filtering of the received time domain signal use the mathematical computations of method <NUM> and method <NUM> in a meaningful way such that the claim as a whole is more than a drafting effort to monopolize the mathematical computation. In particular, the combination of additional elements use the mathematical computations in a specific manner that sufficiently limits the use of the mathematical concepts to the practical application of selecting the optimum channel. Furthermore, selection of the optimum channel is independent of any hardware or software based prior calibration or data dependency or learning.

It is to be understood that the disclosure is extended to a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.

Claim 1:
A processor implemented method (<NUM>) for optimum channel selection in a twin radar with a first channel and a second channel, the method comprising the steps of:
receiving a time domain signal reflected from a subject being monitored, by each of the first channel and the second channel respectively (<NUM>);
filtering, by a low pass filter, the time domain signal received by each of the first channel and the second channel respectively, to obtain a filtered time domain signal having frequencies less than a predetermined frequency corresponding to restful breathing associated with the subject being monitored (<NUM>);
simultaneously computing (<NUM>), via one or more hardware processors,
a peak to peak value (APP<NUM>) of an autocorrelation function (ACF) of the filtered time domain signal associated with the first channel and the second channel respectively, for a predefined time window (406A); and
an average value (VPP<NUM>) of peak to peak amplitude of the filtered time domain signal associated with the first channel and the second channel respectively, for the predefined time window (406B); and
selecting, via the one or more hardware processors, at least one of the first channel and the second channel as the optimum channel based on the computed peak to peak value of the ACF and the average value of the peak to peak amplitude of the filtered time domain signal (<NUM>), wherein the optimum channel reproduces a fundamental frequency, wherein signal of the optimum channel will be more autocorrelated than a null channel and wherein if the second channel has a higher periodicity and auto correlation function than the first channel, then the second channel is chosen as the optimum channel.