How can I know if my conv1D model is overfitted or underfitted from loss curve? I am working on classification of time series multivariate data. By doing PCA, I converted multivariate to uni-variate and fed it into a conv1d in keras. 

However, I am getting a very high accuracy and low loss both in validation and in training. How can I justify this?  

I have tried cross validation, but the results are not much different.
I am using adam optimizer (learning rate:0.0001). With 0.001, my model fails to converge.  

I have made sure that I am not mixing the training and validation datasets.
I have shuffle both datasets independent of each other. I trained on 3728 samples and validated on 610 samples.

Can we expect such a high accuracy with binary classification?
From the curves you are showing yes. Over-fitting would mean that your validation accuracy would be lower than your training accuracy, which is not the case here. Since you say that your training and validation sets are completely independent (i.e. no training samples are present in the validation set) you can consider the results reliable.

However accuracy might not be the best indicator of the model's performance. Make sure your dataset is balanced (i.e. the number of samples in both classes are equal to one another). If not try another metric that better represents the performance of your model.