The high accuracy for HR computed from PPG compared to the ECG baseline from our experiments indicates that PPG is a viable alternative sensor principle for monitoring heart rate in Atlantic salmon. Moreover, the MAX86150 unit appears to be a suitable sensor module for implementation in future DSTs or transmitter tags aiming to measure and report HR over time. Although the sensor is designed for integration in optical HR measurement applications for humans and, thus, with human tissues and blood in mind, the HR estimate relies solely on the time-varying tissue perfusion. While human and fish blood is different with respect to composition and cell morphology, the essential functionality is the same, as is the hemoglobin [40]. The wavelengths emitted by the MAX86150 sensor are chosen due to their absorption sensitivity to oxy- and deoxyhemoglobin which determines the blood’s oxygenation and thus its colour. Because tissue colour is affected by perfusion, processing either or both wavelength will be a feasible approach to obtain HR in both humans and fishes.
The data used in this study originates from 6 different fish, yielding 28 data sets. Although up to 10 data sets were collected from each fish, the data sets are considered independent because each data set is separate in either time, relates to different tissues, or both. When reviewing Table 1, most data available for processing came from the anterior part of the fish. This is probably because the measurements in this region coincide with locations with a high blood supply such as the liver and gut (thus implying high perfusion) compared to the posterior region where tissues with lower perfusion (mostly white muscle tissue) are present. This is supported by that the average SQI (Table 2) was highest for locations and orientations associated with the anterior part of the fish, especially for the 0 degree rotation which is towards the heart. This indicates that future implementations of PPG sensors for Atlantic salmon should facilitate data collection in this area.
The range between the lowest (13.9 BPM) and highest (70.2 BMP) heart rates may be explained by that different fish individuals would have had different physiological baselines (e.g., stress levels and health) prior to the experiments. These differences would, thus, have yielded different individual responses to handling and anesthesia. Differences in heart rates were, therefore, be expected. Reported heart rates for Atlantic salmon and comparable species are between 15 and 80 BPM [21, 37]. With the exception of one individual (13.9 BPM) all measured heart rates fell within this expected range. The individual having a heart rate below 15 could have been more susceptible to sedation thus explaining this result, or the reported HR range is conservative.
In our results, HR was reported using one decimal because additional decimal points are likely inaccurate. This conclusion stems from considering the HR-dependent quantification error for our highest HR (70.2 BPM). When sampling at 200 Hz, the change in BPM resulting from what is considered the maximum timely offset from true peak position can be calculated. This is achieved by first finding beats per second (BPS) which in this case is 70.2/60 = 1.17 BPS. The time between HR peaks will in this case be ∆t = 1/1.17 = 0.855 s. A timely offset in true peak placement exceeding 50% of the sampling interval implies that a peak will be associated with the previous or next sampling point. Hence, for our quantification error we get Eq = 0.855 + (1/200) · 0.5 = 0.8575 s. This peak to peak distance which includes the 50% offset, then gives a shifted BPM of BPM = 60/0.8575 = 69.995. The BPM difference can then be calculated as ∆BPM = 70.2 − 69.995 ≈ 0.2 BPM. The corresponding number for our lowest baseline HR (13.9 BPM) is 0.008 BPM.
The quantification error considerations are closely related to our measurements’ sensitivity. By first accepting that a peak cannot be placed “between” two sampling points, the sensitivity can then be evaluated in the same way as the quantification error, only using the whole sampling interval. Thus, the sensitivity can be considered to be twice that of the quantification error, i.e., 0.4 BPM per sample offset for 70.2 BPM, and 0.016 BPM per sample offset for 13.9 BPM.
The deviation from the baseline of 0.7 ± 1.0% for 660 nm and 1.1 ± 1.2% for 880 nm can be explained by different factors. One potentially important source of error is that PPG is sensitive to motion artefacts. Such artefacts can be divided into two types: Perfusion changes in tissue caused by motion and relative motion between the sensor and the sensing volume. The former is not considered relevant when evaluating the results because all fish were in level 3 anesthesia (surgical) and, thus, motionless. However, such artefacts are likely to be important when the method is applied to free-swimming non-sedated fish. A logical next step on the path towards an operational measurement method would, therefore, be to apply the sensor to fish exhibiting normal swimming behaviour, and collect concurrent PPG and motion data to assess the potential impact of specific motion patterns.
To minimize the effect of the latter, the setup was designed to be as rigid as possible to ensure a stable sensing environment during data collection (Figs. 1 and 3). However, although the setup was mechanically stable and the fish in level 3 anesthesia, motion artifacts caused by potential tissue movement such as peristalsis [40] may have caused transient changes in the trend and potential changes in the amplitude of the PPG’s pulsatile component. This is partly remedied using high pass filtering in the analyses, as this effectually reduces or removes long term trends and any changes therein. Moreover, an estimate of HR relies solely on the frequency content in the PPG signal measurement signal, and not the amplitude. Based on these observations, movement of tissues relative to the sensor during data collection were unlikely to have had impact on the results.
Motion artefacts may also have been caused by tissue contraction (e.g., the heart) if it was within the sensing volume during data collection. This is particularly relevant for the vertical orientation with 0° rotation (i.e., when the sensor was pointing towards the heart). Because this is of particular concern, a subsequent post mortem dissection of Atlantic salmon was done to assess the sensing volume for this orientation and rotation. The dissection revealed that the tissue observed with the present method was likely dominated by low-perfusion fatty tissues surrounding larger blood vessels such as the hepatic arteries and veins (Fig. 7) [40]. Fat has a high optical scattering [41] coefficient due to lipid droplets inside the fat cells. Due to the size of the scatters, this scattering is highly forward directed and almost independent of the wavelength of the light. This implies that the light from the sensor is strongly scattered by the tissue while the intensity decays exponentially with distance from the light source in accordance with the (modified) Beer–Lambert law [42], thus limiting the distance light travels. This is indicated by the fact that the SQI for both wavelengths was generally low due to the big difference between the pulsatile PPG component’s amplitude and the signal mean. A large mean value implies that a lot of light is scattered back to the receiver without having penetrated far into the tissue. It is, therefore, likely that the data originates from tissues close to the light source and that the heart is not part of the sensing volume.
Accuracy may also have been affected by the physiological state of the fish. The fish used in the experiment were lab grown and showed no signs of deteriorated health. The anesthesia had both an analgesic and a paralyzing effect. It must, therefore, be expected that the secondary circulation system driven by the caudal heart and movement was impaired during data collection. In addition, the fish underwent a surgical procedure and the incisions as well as the sensor insertion may have further disturbed parts of the circulation system. When reviewing Table 2 for fish where data for both iterations of the horizontal and vertical 0° are available, similar SQIs for both iterations appear to be the trend. Although this indicates that the physiological state of the fish remained stable during data collection, it is likely that this state differs from that a fully awake and moving fish would exhibit. This underlines the necessity of conducting further experiments with fish exhibiting more normal behaviours and physiological function.
The peak detection procedure may have affected the accuracy of the method because the detrended PPG signals consisted of an oscillating curve with wide peaks compared to the ECG peaks. The accuracy in PPG peak detection was, therefore, lower, thus resulting in slight differences in the intervals between the detected PPG peaks and their corresponding ECG peaks. For long time series containing many peaks, such differences are expected to cancel out, but this may not have been the case for 20 s data sets. This may have been further exacerbated by the fact that the various orientations and rotations would have illuminated tissues and capillary beds supplied by haemal arches connected to different points along the dorsal artery. The resulting differences in pulse transit times (PTT, i.e., times between the heart beat and when it is observable in the sensing volume), may have shifted the PPG in relation to the ECG [43]. Furthermore, if different capillary beds with different PTTs were present in the sensing volume it could have distorted the PPG pulses by dragging them out in time, thereby explaining the differences in morphology seen between the “GOOD” and “FAIR” pulse examples in Fig. 4.
The criteria used for the selection of valid data subsets for analyses are subject to interpretation as highlighted by Elgendi et al. [35], meaning that other evaluators could have included some rejected data sets and vice versa. Although this is an inherent weakness in this method for assessing data quality, it is of greatest importance when using PPG for determination of SpO2 where the PPG shape is paramount for the validity of the SpO2 estimate [24]. When estimating HR only, PPG morphology is of lesser concern since only the frequency content of the pulsatile PPG component is required. The PPG based HR estimate is, therefore, considered robust against variations in interpretation of the subset selection criteria.
Autocorrelation was chosen for PPG analysis because its low computational demand and robustness against potential transient motion artifacts makes it a likely candidate for implementation in a microcontroller suitable for integration in a size and energy constrained fish tag. However, it cannot be ruled out that other, more computationally demanding approaches such as singular spectrum analysis [44], wavelet analysis [45] or a fast Fourier transform [46] approach could have given better results.
The perfusion index (Eq. 3) was used to evaluate signal quality because this is considered the “gold standard” for PPG signal quality evaluation, even though alternative methods (e.g., skewness, kurtosis and entropy) [35] which might lead to better results, exist. Such methods, however, are derived using reference data readily available for humans. To the authors’ knowledge, no reference PPG data for fish exist for comparison. Such data would be a very useful resource in developing new methods for quality evaluations of PPG data from fish, particularly if aspiring to quantify SpO2.
Overall, valid data was identified for all orientations and rotations. Although certain combinations of orientation and rotation yielded fewer data sets fulfilling the subset selection criteria than others, this does not necessarily mean that data are harder to obtain for these orientations and rotations. The same low-energy output settings were used for both orientations and all rotations, thus implying that more valid data could have been obtained if sensor settings, such as output power, had been increased. PPG, therefore, has the potential of being a robust alternative to ECG for HR measurement in fish.